Schedule and publish Instagram and Facebook Reels from Google Sheets with Gemini
n8n-workflow-19738.n8n.io
· n8n.io
Quick overview This workflow runs every five minutes to pick the next due PENDING Reel from Google Sheets, generate Instagram and Facebook captions with Google Gemini, publish the video to Instagram Reels and Facebook Reels via the Meta Graph API, and then mark the item as POSTED in the sheet. How it works 1. Runs every five minutes on a schedule trigger. 2. Reads the next queued Reel from a Google Sheets “Queue” tab and keeps only items whose scheduled publish date/time (Asia/Kolkata) is due and whose status is PENDING. 3. Sends the video URL and content ID to Google Gemini to generate JSON output containing an Instagram caption and a Facebook caption. 4. Creates an Instagram Reel media container with the video URL and the generated Instagram caption. 5. Waits 30 seconds, checks the Instagram processing status, and publishes the Reel to Instagram when it is ready. 6. Starts a Facebook Reels upload session, uploads the video by URL, and publishes it with the generated Facebook descript
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Conformance score: 19/100
F-grade: card is reachable but fails most operational signals.
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Endpoints
| Agent card | https://n8n.io/workflows/19738 |
| Provider | https://n8n.io |
| Docs | https://n8n.io/workflows/19738 |
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Raw agent card JSON
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"alias": [
"Time",
"Scheduler",
"Polling",
"Cron",
"Interval"
],
"resources": {
"generic": [],
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.scheduletrigger/"
}
]
},
"categories": [
"Core Nodes"
],
"nodeVersion": "1.0",
"codexVersion": "1.0"
}
},
"group": "[\"trigger\",\"schedule\"]",
"defaults": {
"name": "Schedule Trigger",
"color": "#31C49F"
},
"iconData": {
"icon": "clock",
"type": "icon"
},
"displayName": "Schedule Trigger",
"typeVersion": 1,
"nodeCategories": [
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 844,
"icon": "fa:filter",
"name": "n8n-nodes-base.filter",
"codex": {
"data": {
"alias": [
"Router",
"Filter",
"Condition",
"Logic",
"Boolean",
"Branch"
],
"details": "The Filter node can be used to filter items based on a condition. If the condition is met, the item will be passed on to the next node. If the condition is not met, the item will be omitted. Conditions can be combined together by AND(meet all conditions), or OR(meet at least one condition).",
"resources": {
"generic": [],
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.filter/"
}
]
},
"categories": [
"Core Nodes"
],
"nodeVersion": "1.0",
"codexVersion": "1.0",
"subcategories": {
"Core Nodes": [
"Flow",
"Data Transformation"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Filter",
"color": "#229eff"
},
"iconData": {
"icon": "filter",
"type": "icon"
},
"displayName": "Filter",
"typeVersion": 2,
"nodeCategories": [
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 1119,
"icon": "fa:robot",
"name": "@n8n/n8n-nodes-langchain.agent",
"codex": {
"data": {
"alias": [
"LangChain",
"Chat",
"Conversational",
"Plan and Execute",
"ReAct",
"Tools"
],
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Agents",
"Root Nodes"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "AI Agent",
"color": "#404040"
},
"iconData": {
"icon": "robot",
"type": "icon"
},
"displayName": "AI Agent",
"typeVersion": 3,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
"id": 1179,
"icon": "fa:code",
"name": "@n8n/n8n-nodes-langchain.outputParserStructured",
"codex": {
"data": {
"alias": [
"json",
"zod"
],
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.outputparserstructured/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Output Parsers"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Structured Output Parser"
},
"iconData": {
"icon": "code",
"type": "icon"
},
"displayName": "Structured Output Parser",
"typeVersion": 1,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
"id": 1237,
"icon": "file:limit.svg",
"name": "n8n-nodes-base.limit",
"codex": {
"data": {
"alias": [
"Limit",
"Remove",
"Slice",
"Transform",
"Array",
"List",
"Item"
],
"details": "",
"resources": {
"generic": [],
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.limit/"
}
]
},
"categories": [
"Core Nodes"
],
"nodeVersion": "1.0",
"codexVersion": "1.0",
"subcategories": {
"Core Nodes": [
"Data Transformation"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Limit"
},
"iconData": {
"type": "file",
"fileBuffer": "data:image/svg+xml;base64,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"
},
"displayName": "Limit",
"typeVersion": 1,
"nodeCategories": [
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 1262,
"icon": "file:google.svg",
"name": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"codex": {
"data": {
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatgooglegemini/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Language Models",
"Root Nodes"
],
"Language Models": [
"Chat Models (Recommended)"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Google Gemini Chat Model"
},
"iconData": {
"type": "file",
"fileBuffer": "data:image/svg+xml;base64,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"
},
"displayName": "Google Gemini Chat Model",
"typeVersion": 1,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
"id": 1281,
"icon": "file:openrouter.svg",
"name": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"codex": {
"data": {
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatopenrouter/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Language Models",
"Root Nodes"
],
"Language Models": [
"Chat Models (Recommended)"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "OpenRouter Chat Model"
},
"iconData": {
"type": "file",
"fileBuffer": "data:image/svg+xml;base64,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"
},
"displayName": "OpenRouter Chat Model",
"typeVersion": 1,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
}
]
},
"detail": {
"description": "Quick overview This workflow runs every five minutes to pick the next due PENDING Reel from Google Sheets, generate Instagram and Facebook captions with Google Gemini, publish the video to Instagram Reels and Facebook Reels via the Meta Graph API, and then mark the item as POSTED in the sheet. How it works 1. Runs every five minutes on a schedule trigger. 2. Reads the next queued Reel from a Google Sheets \u201cQueue\u201d tab and keeps only items whose scheduled publish date/time (Asia/Kolkata) is due and whose status is PENDING. 3. Sends the video URL and content ID to Google Gemini to generate JSON output containing an Instagram caption and a Facebook caption. 4. Creates an Instagram Reel media container with the video URL and the generated Instagram caption. 5. Waits 30 seconds, checks the Instagram processing status, and publishes the Reel to Instagram when it is ready. 6. Starts a Facebook Reels upload session, uploads the video by URL, and publishes it with the generated Facebook description. 7. Updates the matching row in Google Sheets by setting status to POSTED. Setup 1. Connect Google Sheets OAuth credentials and set the spreadsheet/document ID and \u201cQueue\u201d sheet to the one that contains your posting queue. 2. Add a Google Gemini (PaLM) API key credential for the caption-generation step. 3. Add a Meta/Facebook Graph API credential with permissions to publish Reels, and set the correct Instagram Business Account ID and Facebook Page ID used in the publishing requests. 4. Ensure your Google Sheet has columns id, cloudinaryurl (public video URL), publishdate (DD-MM-YYYY), publishtime (HH:mm), and status (use PENDING/POSTED), and that times are intended for the Asia/Kolkata timezone. Requirements - Requirements - n8n - Google Sheets - Google Gemini API - Meta Developer App - Instagram Professional Account - Facebook Page - Public Video URL / Cloudinary URL"
}
}