{ "id": "MRINKwuP8BvQjCU1", "meta": { "templateCredsSetupCompleted": true }, "name": "Automate Research with Decodo, OpenAI, and Gmail", "tags": [], "nodes": [ { "id": "0da43c80-0f67-49cc-a23b-b43502b41f83", "name": "Main Overview", "type": "n8n-nodes-base.stickyNote", "position": [ 5776, -1488 ], "parameters": { "width": 386, "height": 519, "content": "### How it works\nThis workflow automates the collection and analysis of AI/LLM research. It runs on a weekly schedule to:\n1. **Search:** Fetch the latest articles using the Decodo API.\n2. **Scrape:** Extract the full text content from each result, filtering out noise.\n3. **Summarize:** Use an LLM to generate dense, technical summaries of every article.\n4. **Report:** An AI Agent analyzes all summaries to score their relevance and compiles a final intelligence report email.\n\n### Setup steps\n1. **Credentials:** Add your `Decodo` and `OpenAI` API keys in the workflow credentials.\n2. **Schedule:** Adjust the **Weekly Trigger** node to your preferred day and time.\n3. **Search Config:** Update the `search_query` in the **Set Search Config** node if you want to track a different topic.\n4. **Email:** Change the recipient email address in both **Send Email** nodes.\n\n### Customization tips\n- Adjust the \"Relevance Score\" prompt in the **Research Analyst Agent** node to make the filtering stricter or looser." }, "typeVersion": 1 }, { "id": "62878aa3-dc4d-41d0-bd3b-b05462e19258", "name": "Section 1", "type": "n8n-nodes-base.stickyNote", "position": [ 6176, -1328 ], "parameters": { "color": 4, "width": 950, "height": 256, "content": "## 1. Search & Filter\nConfigure the search parameters, fetch results from Google via Decodo, and filter out non-article URLs (e.g., YouTube)." }, "typeVersion": 1 }, { "id": "389d463e-6da6-4d78-a402-771f11878e7d", "name": "Section 2", "type": "n8n-nodes-base.stickyNote", "position": [ 7200, -1312 ], "parameters": { "color": 4, "width": 900, "height": 592, "content": "## 2. Scrape & Sanitize\nLoop through each URL, download the raw HTML, and clean it to remove scripts/styles before passing to the LLM." }, "typeVersion": 1 }, { "id": "9c87b6e3-05d2-486a-8d74-1552ab66269a", "name": "Section 3", "type": "n8n-nodes-base.stickyNote", "position": [ 8176, -1344 ], "parameters": { "color": 4, "width": 480, "height": 480, "content": "## 3. Summarize Content\nGenerate concise, dense summaries for each individual article using an LLM Chain." }, "typeVersion": 1 }, { "id": "1b10ef37-abe3-4a5a-aa8d-0a4a9f78779d", "name": "Section 4", "type": "n8n-nodes-base.stickyNote", "position": [ 8720, -1344 ], "parameters": { "color": 4, "width": 1000, "height": 480, "content": "## 4. Analyze & Report\nAn AI Agent reviews all summaries, ranks them by relevance, and sends the final email report." }, "typeVersion": 1 }, { "id": "4a88cfde-00ef-466f-80e3-f244241c838f", "name": "Weekly Trigger", "type": "n8n-nodes-base.scheduleTrigger", "position": [ 6208, -1232 ], "parameters": { "rule": { "interval": [ { "daysInterval": 7, "triggerAtHour": 6 } ] } }, "typeVersion": 1.2 }, { "id": "8467ff7c-d60a-46f1-8daa-94d895dcd0f3", "name": "Filter & Parse URLs", "type": "n8n-nodes-base.code", "position": [ 7024, -1232 ], "parameters": { "jsCode": "// Safely get the organic results array from Decodo Google Search\n\nconst root = $json.results?.[0]?.content?.results;\n\n// Handle different Decodo response shapes\nconst organic = root?.results?.organic || root?.organic || [];\n\nif (!Array.isArray(organic) || organic.length === 0) {\n return []; // nothing to process\n}\n\n// Filter out YouTube links, then map\nreturn organic\n .filter(item => {\n const url = (item.url || \"\").toLowerCase();\n return !url.includes(\"youtube.com\") && !url.includes(\"youtu.be\");\n })\n .map(item => ({\n json: {\n url: item.url || \"\",\n title: item.title || \"\",\n desc: item.desc || \"\",\n pos: item.pos || item.pos_overall || null,\n url_shown: item.url_shown || \"\",\n favicon_text: item.favicon_text || \"\",\n region: $json.region || \"\",\n platform: $json.platform || \"\"\n }\n }));\n" }, "typeVersion": 2 }, { "id": "f0fa2db8-c14c-4e98-925a-324ccb94610b", "name": "Batch Loop", "type": "n8n-nodes-base.splitInBatches", "position": [ 7232, -1232 ], "parameters": { "options": {} }, "typeVersion": 3 }, { "id": "a9f2008f-883a-489f-8934-a162c5ff0166", "name": "Sanitize HTML", "type": "n8n-nodes-base.code", "position": [ 7936, -1120 ], "parameters": { "mode": "runOnceForEachItem", "jsCode": "const item = $input.item.json;\n\nlet html = \"\";\n\ntry {\n html = item.results[0].content || \"\";\n} catch (e) {\n html = \"\";\n}\n\nlet cleaned = html\n .replace(/]*>[\\s\\S]*?<\\/script>/gi, \"\")\n .replace(/]*>[\\s\\S]*?<\\/style>/gi, \"\")\n .replace(/]*>[\\s\\S]*?<\\/noscript>/gi, \"\")\n .replace(/<\\/?[^>]+>/g, \" \")\n .replace(/ /gi, \" \")\n .replace(/&/gi, \"&\")\n .replace(/"/gi, '\"')\n .replace(/'/gi, \"'\")\n .replace(/</gi, \"<\")\n .replace(/>/gi, \">\")\n .replace(/\\s+/g, \" \")\n .trim();\n\nreturn {\n json: {\n text_clean: cleaned\n }\n};\n" }, "typeVersion": 2 }, { "id": "70fe01b3-73ec-41c5-a8cc-f5f512063e59", "name": "Summarize Chain", "type": "@n8n/n8n-nodes-langchain.chainSummarization", "position": [ 8208, -1248 ], "parameters": { "options": { "summarizationMethodAndPrompts": { "values": { "prompt": "=You will receive cleaned raw text {{ $json.text_clean }} from an AI / LLM related webpage\n(article, blog post, research announcement, release note, or news page).\n\nYour task is to produce a short, dense summary focused ONLY on useful AI / LLM insights.\n\nDo NOT rewrite the entire page.\nDo NOT include marketing fluff, branding, author bios, disclaimers, navigation text, or repeated sections.\n\nExtract ONLY the following information IF it appears in the text:\n\n- Main topic or announcement\n- Model / tool / company name (if any)\n- Type of update (model release, research paper, feature update, benchmark, policy, funding, etc.)\n- Key points or changes (max 3 short bullets)\n- Why it matters (1 short line, practical impact)\n\nReturn the result as a **short human-readable text summary**, NOT JSON.\n\nStyle example:\n\n\u201cTopic: New LLM Model Release \nEntity: Company X \u2013 Model Y \nType: Model release \nKey points: \u2014 ; \u2014 ; \u2014 \nWhy it matters: Faster inference and lower cost for production use.\u201d\n\nIf a field is missing, skip it silently.\nKeep the entire summary under **6\u20138 lines**.\nAvoid extra explanations or speculation.\n", "summarizationMethod": "stuff" } } } }, "typeVersion": 2.1 }, { "id": "9975d7e1-6e4f-434b-b74b-e26f03320f6f", "name": "Aggregate Summaries", "type": "n8n-nodes-base.code", "position": [ 8544, -1248 ], "parameters": { "jsCode": "// Collect all items coming from Summarization/Extraction\nconst items = $input.all();\n\n// Extract each summary from each item\nconst summaries = items.map(item => item.json);\n\n// Build a single output item\nreturn [\n {\n json: {\n summaries\n }\n }\n];\n" }, "typeVersion": 2 }, { "id": "033d4d93-d8f3-4eb1-b843-94fc0bf7138e", "name": "Research Analyst Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 9136, -1248 ], "parameters": { "text": "=You are an AI research analyst.\n\nINPUT YOU WILL RECEIVE:\n1. A collection of short summaries generated from AI / LLM related webpages.\n2. Each summary represents one article, announcement, blog post, or research update.\n3. All content comes from the same search context and timeframe.\n\n-----------------------------------\nSTEP 1 \u2014 Understand the Research Focus\n-----------------------------------\nAssume the goal is to track meaningful developments in:\n- AI models and LLMs\n- AI tooling and infrastructure\n- Research breakthroughs\n- Practical production or business impact\n\n-----------------------------------\nSTEP 2 \u2014 Evaluate Each Summary\n-----------------------------------\nFor EACH summary:\n\n1. Determine whether it is **meaningful or actionable**.\n2. Assign a **Relevance Score (0\u2013100%)** based on:\n - Technical significance\n - Novelty (new model, update, result, or capability)\n - Practical or industry impact\n3. Exclude summaries with relevance below **50%**.\n4. For each included summary, prepare:\n - Topic / Title (if clear)\n - Entity (model, company, or organization)\n - Relevance Score (percentage)\n - 1\u20132 short reasons explaining the score\n\n-----------------------------------\nSTEP 3 \u2014 Generate Final Report\n-----------------------------------\nProduce ONE single consolidated report structured as:\n\nA) High-Level Overview \n - 2\u20133 sentences summarizing the overall AI/LLM trend observed.\n\nB) Key Relevant Updates \n - List items ordered by Relevance Score (highest \u2192 lowest):\n - Topic / Entity\n Relevance: XX%\n Why it matters:\n \u2022 \n \u2022 \n\nC) Notable Patterns or Signals \n - 3\u20135 short bullets highlighting:\n \u2022 Repeated themes\n \u2022 Emerging model types or tools\n \u2022 Research vs industry balance\n \u2022 Signals worth monitoring next week\n\n-----------------------------------\nRULES\n-----------------------------------\n- Output must be plain text (no markdown, no JSON).\n- Do NOT invent facts, models, or companies.\n- Do NOT repeat the same insight in different wording.\n- Keep explanations short and concrete.\n- Maintain a neutral, analytical tone.\n", "options": {}, "promptType": "define" }, "typeVersion": 3 }, { "id": "bf883fd6-ee8c-4bb5-ae8c-747c3bef9545", "name": "Send Email (Report)", "type": "n8n-nodes-base.gmail", "position": [ 9584, -1248 ], "parameters": { "sendTo": "user@example.com", "message": "={{ $json.output }}", "options": { "appendAttribution": false }, "subject": "=Weekly AI / LLM Intelligence Report \u2013 {{ new Date().toLocaleDateString() }}\n", "emailType": "text", "authentication": "serviceAccount" }, "typeVersion": 2.1 }, { "id": "466aa3a8-93ba-480a-abab-a151baecba40", "name": "Search API (Decodo)", "type": "@decodo/n8n-nodes-decodo.decodo", "position": [ 6832, -1232 ], "parameters": { "query": "={{ $json.search_query }}", "operation": "google_search", "results_limit": 1 }, "typeVersion": 1 }, { "id": "161455d9-906d-4492-8a64-f9c4e3e7f29b", "name": "Scrape Content (Decodo)", "type": "@decodo/n8n-nodes-decodo.decodo", "position": [ 7376, -1120 ], "parameters": { "url": "={{$json.url}}" }, "typeVersion": 1 }, { "id": "9774fd35-6fe5-449e-adfc-c13a49fcbd82", "name": "Set Search Config", "type": "n8n-nodes-base.set", "position": [ 6448, -1232 ], "parameters": { "mode": "raw", "options": {}, "jsonOutput": "{\n \"search_query\": \"latest AI LLM news\",\n \"max_results\": 10,\n \"min_relevance_score\": 50,\n \"summary_language\": \"en\",\n \"delivery_channel\": \"email\",\n \"debug_mode\": false\n}\n" }, "typeVersion": 3.4 }, { "id": "b92d812a-ee0a-4e69-98c1-2cdba7cfb437", "name": "Check Content Quality", "type": "n8n-nodes-base.if", "position": [ 7600, -1104 ], "parameters": { "options": {}, "conditions": { "options": { "version": 3, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "606feef1-6b79-4a74-975d-25181e6c2af2", "operator": { "type": "string", "operation": "notEmpty", "singleValue": true }, "leftValue": "={{$json.results?.[0]?.content}}", "rightValue": "" }, { "id": "502031bd-4346-40c0-aee3-aefd744a99f9", "operator": { "type": "number", "operation": "gt" }, "leftValue": "={{$json.results?.[0]?.content.length}}", "rightValue": 300 } ] } }, "typeVersion": 2.3 }, { "id": "f267c3b2-6a23-4aed-a97c-bb91b8fdb56a", "name": "OpenAI Model (Summary)", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 8208, -1040 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4.1-mini" }, "options": {}, "builtInTools": {} }, "typeVersion": 1.3 }, { "id": "150b5b6b-cfc1-4cc9-8b2f-623f9cd4a81f", "name": "Check Results Exist", "type": "n8n-nodes-base.if", "position": [ 8752, -1248 ], "parameters": { "options": {}, "conditions": { "options": { "version": 3, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "4944b6a5-9ddc-4116-a95c-d418f686546c", "operator": { "type": "number", "operation": "gt" }, "leftValue": "={{ $json.summaries.length }}", "rightValue": 0 } ] } }, "typeVersion": 2.3 }, { "id": "a44050d8-28b8-4da0-b907-9631058f8c3d", "name": "Send Email (Empty)", "type": "n8n-nodes-base.gmail", "position": [ 8864, -1072 ], "parameters": { "sendTo": "user@example.com", "message": "=No Updates Found...", "options": { "appendAttribution": false }, "subject": "=Weekly AI / LLM Intelligence Report \u2013 {{ new Date().toLocaleDateString() }}\n", "emailType": "text", "authentication": "serviceAccount" }, "typeVersion": 2.1 }, { "id": "11719afc-dcc6-4435-a212-574152fe9a2b", "name": "OpenAI Model (Agent)", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 9136, -1024 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4.1-mini" }, "options": {}, "builtInTools": {} }, "typeVersion": 1.3 }, { "id": "795a8aae-0023-4a07-8d64-4dcdc47cb821", "name": "Telegram Error Alert", "type": "n8n-nodes-base.telegram", "position": [ 7664, -864 ], "parameters": { "text": "Warning ... Error in scraping ", "chatId": "YOUR_CHAT_ID", "additionalFields": {} }, "typeVersion": 1.2 } ], "active": false, "settings": { "executionOrder": "v1" }, "versionId": "8b7b7448-16b3-44ed-9a07-3ecd8679ccad", "connections": { "Batch Loop": { "main": [ [ { "node": "Summarize Chain", "type": "main", "index": 0 } ], [ { "node": "Scrape Content (Decodo)", "type": "main", "index": 0 } ] ] }, "Sanitize HTML": { "main": [ [ { "node": "Batch Loop", "type": "main", "index": 0 } ] ] }, "Weekly Trigger": { "main": [ [ { "node": "Set Search Config", "type": "main", "index": 0 } ] ] }, "Summarize Chain": { "main": [ [ { "node": "Aggregate Summaries", "type": "main", "index": 0 } ] ] }, "Set Search Config": { "main": [ [ { "node": "Search API (Decodo)", "type": "main", "index": 0 } ] ] }, "Aggregate Summaries": { "main": [ [ { "node": "Check Results Exist", "type": "main", "index": 0 } ] ] }, "Check Results Exist": { "main": [ [ { "node": "Research Analyst Agent", "type": "main", "index": 0 } ], [ { "node": "Send Email (Empty)", "type": "main", "index": 0 } ] ] }, "Filter & Parse URLs": { "main": [ [ { "node": "Batch Loop", "type": "main", "index": 0 } ] ] }, "Search API (Decodo)": { "main": [ [ { "node": "Filter & Parse URLs", "type": "main", "index": 0 } ] ] }, "OpenAI Model (Agent)": { "ai_languageModel": [ [ { "node": "Research Analyst Agent", "type": "ai_languageModel", "index": 0 } ] ] }, "Check Content Quality": { "main": [ [ { "node": "Sanitize HTML", "type": "main", "index": 0 } ], [ { "node": "Telegram Error Alert", "type": "main", "index": 0 } ] ] }, "OpenAI Model (Summary)": { "ai_languageModel": [ [ { "node": "Summarize Chain", "type": "ai_languageModel", "index": 0 } ] ] }, "Research Analyst Agent": { "main": [ [ { "node": "Send Email (Report)", "type": "main", "index": 0 } ] ] }, "Scrape Content (Decodo)": { "main": [ [ { "node": "Check Content Quality", "type": "main", "index": 0 } ] ] } } }