Reply to Facebook ad comments with OpenRouter AI, Google Docs and Slack

by Salman Mehboob ✓ · 309 views · created 2026-04-04 · Social Media, AI Chatbot

What this workflow does

This AI workflow has 14 nodes and runs when an HTTP POST request hits its webhook URL. Along the way it connects OpenRouter Chat Model, Google Docs and Slack.

How it runs

Trigger: When an HTTP POST request hits its webhook URL.

1. Filleter Author Comment and Reqular Post (Filter): keeps only the items that match a condition
2. Get Commnet Details (HTTP Request): calls an HTTP API
3. Skip If Coment Contains Attachment (Switch): routes items into 2 branches
4. Get FB Post Data (HTTP Request): calls an HTTP API
5. Clean Data (Edit Fields (Set)): sets 6 fields: post_text, post_permalink_url, comment_text…
6. Comment Classifier (AI Agent): runs an AI agent, with model: OpenRouter Chat Model; output parser: Structured Output Parser
7. Switch: routes items into 4 branches
8. Comment Generator (AI Agent): runs an AI agent, with model: OpenRouter Chat Model; 1 tool (Knowledge Base)
9. Inform User (Slack): uses Slack
10. Reply to Comment (HTTP Request): calls an HTTP API (POST)

Services it uses

OpenRouter Chat ModelGoogle DocsSlack

Before you upgrade to n8n 3.0

Nodes used (10)

• Webhookn8n-nodes-base.webhook
• HTTP Requestn8n-nodes-base.httpRequest
• OpenRouter Chat Model@n8n/n8n-nodes-langchain.lmChatOpenRouter
• Filtern8n-nodes-base.filter
• Switchn8n-nodes-base.switch
• Edit Fields (Set)n8n-nodes-base.set
• AI Agent@n8n/n8n-nodes-langchain.agent
• Google Docs Tooln8n-nodes-base.googleDocsTool
• Slackn8n-nodes-base.slack
• Structured Output Parser@n8n/n8n-nodes-langchain.outputParserStructured

From the author

Every comment on your Facebook ad is a sales opportunity. But manually replying to dozens of comments every day - in the right tone, in the right language, at the right speed - is simply not scalable. This workflow deploys an AI agent that watches your…

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