This article provides a complete, practical walkthrough of the Splitout Googlecalendar 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:**  
   Streamline Meeting Management with AI: A Smart n8n Workflow for Google Meet Transcripts and Automated Follow-Ups
   
   **Meta Description:**  
   Discover how to automate meeting follow-ups using this n8n workflow powered by OpenAI, Google Calendar, and Google Meet. Learn how to extract transcripts, summarize discussions, and create calendar events intelligently.
   
   **Keywords:**  
   n8n workflow, AI meeting assistant, Google Meet transcript automation, OpenAI, Google Calendar automation, meeting follow-up automation, transcript summarization, langchain agent, AI calendar tool, meeting workflow n8n
   
   ---
   
   ### Automating Meeting Follow-Ups Using n8n, Google APIs, and AI
   
   Meetings are the engines powering collaboration in modern organizations — but managing what comes after a meeting often requires more work than the meeting itself. What if you could automate that? This article explores a powerful n8n workflow that leverages AI and various third-party services to summarize meetings, extract action items, and even schedule follow-ups — all without human intervention.
   
   Built entirely within n8n, this workflow retrieves Google Meet transcripts, uses OpenAI to summarize the content, and performs intelligent actions such as creating new calendar invites — a perfect example of how no-code tools can empower users to offload busywork to automated agents.
   
   ---
   
   ### Step-by-Step Breakdown of the Workflow
   
   ##### 1. Fetching the Calendar Event
   The process begins with the “Get Calendar Event” node which retrieves an existing Google Calendar event by its ID. This event typically contains the Google Meet link and other metadata. Since Google Meet transcripts reside in Google Drive via calendar-linked recordings, this is a necessary starting point.
   
   ##### 2. Retrieving the Meeting Transcript
   Once the event is retrieved, the workflow queries the Google Meet API to fetch the associated `conferenceRecords`. Through a combination of HTTP nodes, it then gets the transcript location and retrieves the file from Google Drive.
   
   The transcript, typically saved in PDF format, is then processed using the “PDF Loader” to extract its text content. This prepares it to be parsed by natural language processing systems.
   
   ##### 3. AI-Driven Summarization and Decision Making
   Here’s where the magic begins. The workflow hands over the transcript (along with meeting metadata) to a LangChain-based AI Agent node, powered by OpenAI's GPT model. This intelligent assistant follows a system prompt with detailed instructions:
   
   - Summarize the meeting content.
   - Highlight key information shared by each attendee.
   - Identify all next steps (or suggest new ones).
   - Take relevant actions such as scheduling follow-up meetings.
   
   The agent uses a structured parser to output the results in JSON format, which ensures the next nodes can interpret the AI output reliably.
   
   ##### 4. Triggering Follow-Up Actions
   The LangChain Agent determines whether a follow-up event is required. If so, it uses the “Schedule Meeting” tool, a workflow-integrated API endpoint, to create the next meeting. The parameters — including date, time, title, description, and attendees — are passed dynamically based on the agent’s analysis of the transcript.
   
   Following the creation of the event, a loop is triggered via the “Attendees List” node to add each attendee to the new calendar invite using the “Add Attendee to Invite” node. All this happens programmatically, saving hours of manual work.
   
   ---
   
   ### Real-World Use Case
   
   Imagine leading a project status meeting where action items are flying left and right. Instead of pausing to jot down notes or assign tasks manually, you end the meeting knowing that an AI assistant will:
   
   - Summarize discussion points clearly.
   - Outline each person’s contributions.
   - Recommend next steps or detect follow-up commitments.
   - Schedule the subsequent meeting and invite relevant participants.
   
   The entire cycle is executed without lifting a finger after the meeting ends.
   
   ---
   
   ### Technologies and APIs Used
   
   This workflow combines several services to deliver end-to-end automation:
   
   | Service             | Purpose                                                 |
   |---------------------|---------------------------------------------------------|
   | Google Calendar API | To fetch event metadata and schedule new meetings       |
   | Google Meet API     | To retrieve the transcript associated with the meeting  |
   | Google Drive API    | To download the transcript file                         |
   | OpenAI API          | To analyze, summarize, and trigger follow-up actions    |
   | n8n LangChain Tools | Enable function-calling AI agents for structured output |
   
   ---
   
   ### Takeaway
   
   This n8n workflow showcases how today’s SaaS integrations and AI capabilities can be chained together to boost workplace efficiency dramatically. It's not just a bot — it's a workflow designed to think, analyze, and act, all within secure and user-configurable boundaries.
   
   By leveraging Google’s ecosystem and OpenAI models in a no-code environment, team leads and developers can save hours of administrative strain, reduce information loss, and streamline meeting productivity.
   
   ---
   
   Tired of post-meeting chaos? Let this AI solution tame your calendar. Want to try it out? All you need is an n8n instance, valid API credentials, and a little curiosity.
   
   ---
   
   **Need Help?**  
   Stuck setting up the credentials or tweaking the JSON schema for the AI Agent? Join the [n8n Discord](https://discord.com/invite/XPKeKXeB7d) or get support from the [Community forum](https://community.n8n.io/).
   
   Happy automating! 🚀
   
   ---
   
   ### List of Third-Party APIs Used:
   
   - Google Calendar API
   - Google Meet (conferenceRecords) API
   - Google Drive API
   - OpenAI API (via LangChain and n8n)
   - LangChain Agent and Structured Output Parser (via n8n LangChain nodes)

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.