Ranjan Dailata This workflow corresponds to n8n.io template #10962 — we link there as the canonical source.
This workflow follows the Chainllm → Google Sheets recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "pIU4FdK9iQpgw6lx",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Extract, Summarize, Sentiment Analysis of Price Drops for Amazon Products via Decodo",
"tags": [
{
"id": "Kujft2FOjmOVQAmJ",
"name": "Engineering",
"createdAt": "2025-04-09T01:31:00.558Z",
"updatedAt": "2025-04-09T01:31:00.558Z"
},
{
"id": "ZOwtAMLepQaGW76t",
"name": "Building Blocks",
"createdAt": "2025-04-13T15:23:40.462Z",
"updatedAt": "2025-04-13T15:23:40.462Z"
},
{
"id": "ddPkw7Hg5dZhQu2w",
"name": "AI",
"createdAt": "2025-04-13T05:38:08.053Z",
"updatedAt": "2025-04-13T05:38:08.053Z"
}
],
"nodes": [
{
"id": "2b808791-791d-4c97-969d-d1671564230e",
"name": "When clicking \u2018Test workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-304,
-688
],
"parameters": {},
"typeVersion": 1
},
{
"id": "4876fb18-7db4-4062-9f66-2dc17a251851",
"name": "Set input fields",
"type": "n8n-nodes-base.set",
"position": [
-112,
-688
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "0ac91db2-9848-40d4-b942-cd7288597ded",
"name": "price_drop_url",
"type": "string",
"value": "https://camelcamelcamel.com/top_drops?t=daily"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "67595908-efad-4799-ab32-c5030ca8cdb8",
"name": "Split Out",
"type": "n8n-nodes-base.splitOut",
"position": [
736,
-688
],
"parameters": {
"options": {},
"fieldToSplitOut": "output"
},
"typeVersion": 1
},
{
"id": "9a4c555a-45ae-4126-8aa0-dec77c515e9d",
"name": "Loop Over Items",
"type": "n8n-nodes-base.splitInBatches",
"position": [
960,
-688
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "a47aedbc-e23a-483d-a0ad-cbe84a5f0f08",
"name": "Sentiment Analysis",
"type": "@n8n/n8n-nodes-langchain.informationExtractor",
"position": [
1536,
-512
],
"parameters": {
"text": "=Perform a detailed sentiment analysis on the following content: {{ $json.results[0].content }}\n\nYour task is to:\n\n1. Determine overall sentiment\n\nClassify the sentiment strictly as:\npositive, neutral, or negative\n\nBase the classification on the dominant emotional tone of the text.\n\n2. Compute a sentiment polarity score\n\nProvide a numeric score between -1 and 1, where:\n\n-1 = very negative\n\n0 = neutral\n\n1 = very positive\n\nScore must reflect intensity and confidence of sentiment.\n\n3. Extract key topics\n\nIdentify important aspects, themes, or points discussed in the content.\nExamples: battery life, build quality, delivery, support, price, performance, etc.\n\nTopics must be:\n\nconcise\n\nrelevant\n\nnon-duplicated\n\nlowercase unless they are proper nouns\n\nOutput Rules\n\nOutput valid JSON only, following the schema below exactly.\n\nDo not include commentary, markdown, or explanation outside the JSON object.",
"options": {},
"schemaType": "manual",
"inputSchema": "{\n \"$schema\": \"http://json-schema.org/schema#\",\n \"title\": \"EcommerceSentimentSubset\",\n \"type\": \"object\",\n \"properties\": {\n \"sentiment\": {\n \"type\": \"string\",\n \"enum\": [\"positive\", \"neutral\", \"negative\"],\n \"description\": \"Categorized sentiment from NLP analysis\"\n },\n \"sentimentScore\": {\n \"type\": \"number\",\n \"minimum\": -1,\n \"maximum\": 1,\n \"description\": \"Numeric sentiment polarity score (-1 = very negative, 1 = very positive)\"\n },\n \"topics\": {\n \"type\": \"array\",\n \"description\": \"Key aspects mentioned in the review (e.g., battery, build quality, delivery)\",\n \"items\": {\n \"type\": \"string\"\n }\n }\n }\n}\n"
},
"retryOnFail": true,
"typeVersion": 1.1
},
{
"id": "c10604a6-868a-49b7-a4f2-eafe32686eaa",
"name": "Merge",
"type": "n8n-nodes-base.merge",
"position": [
1904,
-608
],
"parameters": {},
"typeVersion": 3.1
},
{
"id": "4ee0392f-7f59-4d1f-8c64-b8a090e57c91",
"name": "Update Google Sheets",
"type": "n8n-nodes-base.googleSheets",
"position": [
2384,
-448
],
"parameters": {
"columns": {
"value": {
"output": "={{ $json.data.toJsonString() }}"
},
"schema": [
{
"id": "output",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "output",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"output"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "appendOrUpdate",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/10gAihQMT8-h8Mpehe9j-xxN4oTTpg8qwToI-I-Eauew/edit#gid=0",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1a1yb4XSMQ0Vs0Rg2RCwrcIVXwDN3ImXW_4OUebURKZI",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1a1yb4XSMQ0Vs0Rg2RCwrcIVXwDN3ImXW_4OUebURKZI/edit?usp=drivesdk",
"cachedResultName": "Pricedrop Info"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 4.5
},
{
"id": "4e71b068-a290-4334-8e73-cc62668d366c",
"name": "Structured Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
528,
-464
],
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"id\": {\n \"type\": \"string\"\n },\n \"title\": {\n \"type\": \"string\"\n },\n \"price\": {\n \"type\": \"string\"\n },\n \"savings\": {\n \"type\": [\"string\", \"null\"]\n },\n \"link\": {\n \"type\": \"string\"\n }\n },\n \"required\": [\"id\", \"title\", \"price\", \"link\"]\n }\n}\n"
},
"typeVersion": 1.2
},
{
"id": "f2d73087-0763-4c18-9d38-576d40b31449",
"name": "Structure Data Extract Using LLM",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
336,
-688
],
"parameters": {
"text": "=Extract clean, structured JSON data from the following text:\n\n{{ $json.results[0].content }}\n\nYour goal is to identify all product entries and convert them into a normalized JSON array.",
"batching": {},
"promptType": "define",
"hasOutputParser": true
},
"retryOnFail": true,
"typeVersion": 1.7
},
{
"id": "54baaa48-901d-4b26-934c-c5accbd2718b",
"name": "Aggregate",
"type": "n8n-nodes-base.aggregate",
"position": [
2096,
-608
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1
},
{
"id": "c3feeb14-e1cc-49ff-a5bc-a39ea37fdefe",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
1872,
-1040
],
"parameters": {
"color": 7,
"width": 736,
"height": 848,
"content": ""
},
"typeVersion": 1
},
{
"id": "b6835025-d8a3-4e73-9475-4f267bdf1022",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
720,
-1040
],
"parameters": {
"color": 7,
"width": 1128,
"height": 852,
"content": "\n"
},
"typeVersion": 1
},
{
"id": "a8ed3842-b93a-4129-855b-2d87c5c9e24b",
"name": "Decodo",
"type": "@decodo/n8n-nodes-decodo.decodo",
"position": [
80,
-688
],
"parameters": {
"geo": "=",
"url": "={{ $json.price_drop_url }}",
"headless": false,
"markdown": true
},
"credentials": {
"decodoApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "ba4495df-dfe5-4ccb-9b06-08dcfa7f5fc4",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
288,
-448
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "d07b1d82-1793-49c5-b4f4-c87f45452200",
"name": "Decodo Loop Web Scraper",
"type": "@decodo/n8n-nodes-decodo.decodo",
"position": [
1328,
-672
],
"parameters": {
"geo": "=",
"url": "={{ $json.link }}",
"headless": false,
"markdown": true
},
"credentials": {
"decodoApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "92b5cea7-6a8a-43c4-a19d-a1aaf75cea33",
"name": "OpenAI Chat Model for Sentiment Analysis",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1552,
-336
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "50b80423-6a9f-4030-9a67-e3ce7b150ec7",
"name": "Summarize Content",
"type": "@n8n/n8n-nodes-langchain.informationExtractor",
"position": [
1536,
-880
],
"parameters": {
"text": "=Generate a clear, accurate, and comprehensive summary of the following content: - {{ $json.results[0].content }}\n\nYour summary must:\n\nCapture all key points, major themes, and important details.\n\nPreserve the original meaning without introducing assumptions.\n\nBe concise, well-structured, and written in professional language.\n\nEliminate redundancy, filler text, and irrelevant information.\n\nPresent information in a logically organized flow.\n\nEnsure the summary is a single coherent paragraph unless the content requires multiple sections.\n\nDo not include explanations, markdown, or any text outside the JSON object.",
"options": {},
"schemaType": "manual",
"inputSchema": "{\n\t\"type\": \"object\",\n\t\"properties\": {\n\t\t\"comprehensive_summary\": {\n\t\t\t\"type\": \"string\"\n\t\t}\n\t}\n}"
},
"retryOnFail": true,
"typeVersion": 1.2
},
{
"id": "305690dc-1997-4281-8821-3a61ddc8c95f",
"name": "OpenAI Chat Model for Summarize Content",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1600,
-704
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "2a6a5cb8-44b1-450b-9f05-79c088f86b1b",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
48,
-1040
],
"parameters": {
"color": 7,
"width": 656,
"height": 848,
"content": "\n\n"
},
"typeVersion": 1
},
{
"id": "56251a77-b7ba-404c-833f-c4f888a84734",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-992,
-1248
],
"parameters": {
"color": 3,
"width": 640,
"height": 288,
"content": "## Disclaimer\n\n\n\nThis workflow is only available on n8n self-hosted as it's making use of the community node for the Decodo Web Scraping"
},
"typeVersion": 1
},
{
"id": "103c8fe6-8438-4a63-b3b0-55beaec93ce6",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-992,
-944
],
"parameters": {
"width": 640,
"height": 752,
"content": "## **How It Works**\n\nThis workflow pulls daily Amazon price-drop data from a Decodo-scraped source, extracts structured product information, and then loops through each product to scrape its corresponding page for deeper context. \n\nFor every item, the workflow generates two outputs: a concise summary and a sentiment analysis. All aggregated results are merged and pushed into a Google Sheet, making it easy to monitor trends, compare products, or trigger further automations.\n\n## **Setup**\n\n- Add your Decodo and OpenAI credentials.\n\n- Update the **price_drop_url** in the Set Input Fields node if needed.\n\n- Connect your Google Sheets OAuth credential and map the correct sheet.\n\n- Adjust the summary/sentiment schemas based on the level of detail you need.\n\n\n## **Customize**\n\n* Change the **price_drop_url** URL within the **Set input fields** node\n* Modify extraction schema within the **Structure Data Extract Using LLM** node\n* Adjust summary/sentiment prompts\n* Update Google Sheets mapping\n\n"
},
"typeVersion": 1
},
{
"id": "6bb72b4f-2d2b-44cb-8ae8-1401067e0e8a",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
48,
-1280
],
"parameters": {
"color": 6,
"width": 656,
"height": 224,
"content": "## 1. Structured Data Extraction\n\nPerform a structured data extraction using the OpenAI gpt-4.1-mini. \n\n- Analyze and extract the structured data as per the Decodo Web Scraped Content. \n\n- Decodo\u2019s raw scrape is converted into a clean, consistent product structure using OpenAI. This ensures every item has normalized fields (title, price, link, savings, etc.) before entering the loop."
},
"typeVersion": 1
},
{
"id": "b5361706-de8a-42d9-a96e-e8ac32c65b91",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
720,
-1280
],
"parameters": {
"color": 6,
"width": 1136,
"height": 224,
"content": "\n## 2. Loop and Extract Data\n\nPerform Summarization & Sentiment Analysis with Decodo and OpenAI LLM. \n\n- Each product is processed individually using SplitOut + Batches. \n\n- For every item, the workflow performs a deep Decodo scrape of the product page, then uses OpenAI to generate a structured summary and sentiment analysis. \n\n- This step produces item-level insights used in the final output."
},
"typeVersion": 1
},
{
"id": "472decee-f819-4d84-9b0a-3caa01525c1a",
"name": "Sticky Note7",
"type": "n8n-nodes-base.stickyNote",
"position": [
1872,
-1280
],
"parameters": {
"color": 6,
"width": 736,
"height": 224,
"content": "## 3. Output Data Handling \n\nMerge, Aggregate and output content to Google Speadsheet. \n\n- Results from summarization and sentiment analysis are merged and aggregated into a single dataset.\n- The workflow then writes each processed item to Google Sheets, providing a structured, continuously updated record of all extracted product insights."
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "61b4baa7-e0a8-40a4-bc1b-0f9de57ebbdd",
"connections": {
"Merge": {
"main": [
[
{
"node": "Aggregate",
"type": "main",
"index": 0
}
]
]
},
"Decodo": {
"main": [
[
{
"node": "Structure Data Extract Using LLM",
"type": "main",
"index": 0
}
]
]
},
"Aggregate": {
"main": [
[
{
"node": "Update Google Sheets",
"type": "main",
"index": 0
}
]
]
},
"Split Out": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "Decodo Loop Web Scraper",
"type": "main",
"index": 0
}
]
]
},
"Set input fields": {
"main": [
[
{
"node": "Decodo",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Structure Data Extract Using LLM",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Summarize Content": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 0
}
]
]
},
"Sentiment Analysis": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 1
}
]
]
},
"Update Google Sheets": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Decodo Loop Web Scraper": {
"main": [
[
{
"node": "Sentiment Analysis",
"type": "main",
"index": 0
},
{
"node": "Summarize Content",
"type": "main",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "Structure Data Extract Using LLM",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Structure Data Extract Using LLM": {
"main": [
[
{
"node": "Split Out",
"type": "main",
"index": 0
}
]
]
},
"When clicking \u2018Test workflow\u2019": {
"main": [
[
{
"node": "Set input fields",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model for Summarize Content": {
"ai_languageModel": [
[
{
"node": "Summarize Content",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"OpenAI Chat Model for Sentiment Analysis": {
"ai_languageModel": [
[
{
"node": "Sentiment Analysis",
"type": "ai_languageModel",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
decodoApigoogleSheetsOAuth2ApiopenAiApi
Pro
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About this workflow
This workflow automatically scrapes Amazon price-drop data via Decodo, extracts structured product details with OpenAI, generates summaries and sentiment insights for each item, and saves everything to Google Sheets — creating a fully automated price-intelligence pipeline.
Source: https://n8n.io/workflows/10962/ — original creator credit. Request a take-down →
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