AutomationFlowsAI & RAG › Create Content Strategies with Dual AI Research Agents Using Serpapi, Gpt-4…

Create Content Strategies with Dual AI Research Agents Using Serpapi, Gpt-4…

Original n8n title: Create Content Strategies with Dual AI Research Agents Using Serpapi, Gpt-4 & Sheets

ByMarth - Business Automation @marth on n8n.io

This workflow is a comprehensive, two-AI-agent system that acts as a virtual research and content team. It automates the entire process of turning a single topic into an actionable content strategy. AI Agent 1 (The Researcher): The workflow starts with a manual trigger where you…

Cron / scheduled trigger★★★★☆ complexityAI-powered12 nodesSlackGoogle SheetsOpenAIN8N Nodes Serpapi
AI & RAG Trigger: Cron / scheduled Nodes: 12 Complexity: ★★★★☆ AI nodes: yes Added:

This workflow corresponds to n8n.io template #6519 — we link there as the canonical source.

This workflow follows the Google Sheets → OpenAI recipe pattern — see all workflows that pair these two integrations.

The workflow JSON

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{
  "id": "hb0hz8bXmayE1qVE",
  "name": "Automated Two-AI-Agent Content Research & Strategy System with SerpApi & GPT-4",
  "tags": [],
  "nodes": [
    {
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      "name": "Schedule Trigger",
      "type": "n8n-nodes-base.scheduleTrigger",
      "position": [
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        -64
      ],
      "parameters": {},
      "typeVersion": 1.2
    },
    {
      "id": "013aadf1-d0db-4f67-a21d-59756a9e0a6f",
      "name": "Team Notification",
      "type": "n8n-nodes-base.slack",
      "position": [
        1296,
        32
      ],
      "parameters": {},
      "typeVersion": 2.3
    },
    {
      "id": "73456486-adbf-4325-9aea-fe8eb6acc684",
      "name": "Store Ideas",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
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        -160
      ],
      "parameters": {},
      "typeVersion": 4.6
    },
    {
      "id": "c6a0b4f8-1fef-40cd-8dba-6d435f229cfc",
      "name": "Parse AI Output",
      "type": "n8n-nodes-base.code",
      "position": [
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      ],
      "parameters": {},
      "typeVersion": 2
    },
    {
      "id": "825aabd9-8644-4850-89b7-d686429c662a",
      "name": "The Strategist Agent",
      "type": "@n8n/n8n-nodes-langchain.openAi",
      "position": [
        672,
        -64
      ],
      "parameters": {},
      "typeVersion": 1.8
    },
    {
      "id": "99c23bcc-66a1-4deb-a086-2324daf93592",
      "name": "Data Aggregation",
      "type": "n8n-nodes-base.code",
      "position": [
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        -64
      ],
      "parameters": {},
      "typeVersion": 2
    },
    {
      "id": "58d42e44-fc20-484e-9eab-a91e61799aa8",
      "name": "The Researcher Agent",
      "type": "n8n-nodes-serpapi.serpApi",
      "position": [
        224,
        -64
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "c32e13d6-6486-426e-a466-acacf286899d",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        176,
        -144
      ],
      "parameters": {
        "content": ""
      },
      "typeVersion": 1
    },
    {
      "id": "08345e0b-a09c-498f-9f8f-dee0d7aa96ef",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      ],
      "parameters": {
        "content": ""
      },
      "typeVersion": 1
    },
    {
      "id": "234a449a-51d3-4d94-9362-13efd83595c5",
      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1024,
        -240
      ],
      "parameters": {
        "content": ""
      },
      "typeVersion": 1
    },
    {
      "id": "ea644149-27f9-400f-955b-f8f69487a9fe",
      "name": "Sticky Note3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -48,
        -144
      ],
      "parameters": {
        "content": ""
      },
      "typeVersion": 1
    },
    {
      "id": "92d60124-adb8-4925-9164-a2af9de5832f",
      "name": "Sticky Note4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -48,
        176
      ],
      "parameters": {
        "content": ""
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "e407032a-c09b-4724-a292-07eaaf4d2e69",
  "connections": {
    "Parse AI Output": {
      "main": [
        [
          {
            "node": "Store Ideas",
            "type": "main",
            "index": 0
          },
          {
            "node": "Team Notification",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Data Aggregation": {
      "main": [
        [
          {
            "node": "The Strategist Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Schedule Trigger": {
      "main": [
        [
          {
            "node": "The Researcher Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "The Researcher Agent": {
      "main": [
        [
          {
            "node": "Data Aggregation",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "The Strategist Agent": {
      "main": [
        [
          {
            "node": "Parse AI Output",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}
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About this workflow

This workflow is a comprehensive, two-AI-agent system that acts as a virtual research and content team. It automates the entire process of turning a single topic into an actionable content strategy. AI Agent 1 (The Researcher): The workflow starts with a manual trigger where you…

Source: https://n8n.io/workflows/6519/ — original creator credit. Request a take-down →

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