{"workflow":{"id":12074,"name":"Create weekly AI research briefings with Decodo, OpenAI, and Gmail","views":41,"recentViews":0,"totalViews":41,"createdAt":"2025-12-23T16:30:29.127Z","description":"## What Problem Does It Solve?\n- Staying up-to-date with AI and LLM developments requires reading dozens of articles every week.\n- Manual research is time-consuming and often leads to “information overload” or reading low-quality clickbait.\n- Important technical breakthroughs often get buried under marketing fluff.\n\nThis workflow solves these by:\n- Leveraging **Decodo** to instantly find and scrape high-quality organic articles (automatically filtering out YouTube/video noise).\n- Using AI to read and summarize every article individually.\n- Using an \"Analyst Agent\" to score news by relevance and write a single, high-quality intelligence report.\n\n## How to Configure It\n\n### Decodo & API Setup\n- **Decodo:** Connect your Decodo credentials. This is the core engine that handles the high-precision Google Search and content scraping.\n- **OpenAI:** Connect your OpenAI API key (GPT-4o or 4.1-mini recommended for best analysis).\n- **Gmail:** Connect a Google Service Account or Gmail OAuth to send the emails.\n\n### Search Configuration\n- Open the **Set Search Config** node.\n- Edit the `search_query` value to match your niche (e.g., \"Latest Large Language Model benchmarks\" or \"Generative AI in Healthcare\").\n\n## How It Works\n- **Trigger:** The workflow wakes up once a week (customizable).\n- **Search (Powered by Decodo):** It searches Google using Decodo's organic results filter to ensure only high-quality reading material is selected.\n- **Scraping:** It visits every URL found and extracts the raw text, cleaning up HTML tags.\n- **Summarization:** An LLM reads each article individually to extract key technical points.\n- **Analyst Agent:** Reviews all summaries, assigns a \"Relevance Score\", and compiles the final newsletter.\n- **Delivery:** The final report is emailed to you immediately.\n\n## Customization Ideas\n- Change the topic to any industry (Crypto, Finance, Sports).\n- Swap the AI model for Claude or DeepSeek.\n- Log the summaries into a Notion database.\n\nIf you need any help [Get In Touch](https://www.linkedin.com/in/abdallaelshikh0/)","workflow":{"id":"MRINKwuP8BvQjCU1","meta":{"instanceId":"f1978771fd34bc46e36d87c1acc8871823f5bf69147a32202210bfd1bdfe4090","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(/<script[^>]*>[\\s\\S]*?<\\/script>/gi, \"\")\n  .replace(/<style[^>]*>[\\s\\S]*?<\\/style>/gi, \"\")\n  .replace(/<noscript[^>]*>[\\s\\S]*?<\\/noscript>/gi, \"\")\n  .replace(/<\\/?[^>]+>/g, \" \")\n  .replace(/&nbsp;/gi, \" \")\n  .replace(/&amp;/gi, \"&\")\n  .replace(/&quot;/gi, '\"')\n  .replace(/&#39;/gi, \"'\")\n  .replace(/&lt;/gi, \"<\")\n  .replace(/&gt;/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“Topic: New LLM Model Release  \nEntity: Company X – Model Y  \nType: Model release  \nKey points: — ; — ; —  \nWhy it matters: Faster inference and lower cost for production use.”\n\nIf a field is missing, skip it silently.\nKeep the entire summary under **6–8 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 — 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 — Evaluate Each Summary\n-----------------------------------\nFor EACH summary:\n\n1. Determine whether it is **meaningful or actionable**.\n2. Assign a **Relevance Score (0–100%)** 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   - 1–2 short reasons explaining the score\n\n-----------------------------------\nSTEP 3 — Generate Final Report\n-----------------------------------\nProduce ONE single consolidated report structured as:\n\nA) High-Level Overview  \n   - 2–3 sentences summarizing the overall AI/LLM trend observed.\n\nB) Key Relevant Updates  \n   - List items ordered by Relevance Score (highest → lowest):\n     - Topic / Entity\n       Relevance: XX%\n       Why it matters:\n         • <reason 1>\n         • <reason 2>\n\nC) Notable Patterns or Signals  \n   - 3–5 short bullets highlighting:\n     • Repeated themes\n     • Emerging model types or tools\n     • Research vs industry balance\n     • 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],"webhookId":"02972ab1-09c7-4533-8c0f-b946180779e4","parameters":{"sendTo":"user@example.com","message":"={{ $json.output }}","options":{"appendAttribution":false},"subject":"=Weekly AI / LLM Intelligence Report – {{ 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],"webhookId":"f35b4762-a142-471a-8f79-7db275f2470c","parameters":{"sendTo":"user@example.com","message":"=No Updates Found...","options":{"appendAttribution":false},"subject":"=Weekly AI / LLM Intelligence Report – {{ 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],"webhookId":"9d4f4c2b-3223-4bad-9ec6-bff08fa7626e","parameters":{"text":"Warning ... Error in scraping ","chatId":"YOUR_CHAT_ID","additionalFields":{}},"typeVersion":1.2}],"active":false,"pinData":{},"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}]]}}},"lastUpdatedBy":1,"workflowInfo":{"nodeCount":22,"nodeTypes":{"n8n-nodes-base.if":{"count":2},"n8n-nodes-base.set":{"count":1},"n8n-nodes-base.code":{"count":3},"n8n-nodes-base.gmail":{"count":2},"n8n-nodes-base.telegram":{"count":1},"n8n-nodes-base.stickyNote":{"count":5},"n8n-nodes-base.splitInBatches":{"count":1},"@n8n/n8n-nodes-langchain.agent":{"count":1},"n8n-nodes-base.scheduleTrigger":{"count":1},"@decodo/n8n-nodes-decodo.decodo":{"count":2},"@n8n/n8n-nodes-langchain.lmChatOpenAi":{"count":2},"@n8n/n8n-nodes-langchain.chainSummarization":{"count":1}}},"status":"published","readyToDemo":null,"user":{"name":"Abdullah Alshiekh","username":"abdullah01","bio":"🚀 Automation pro building AI-powered workflows with n8n.\n💼 Special focus on real use cases\n🔧 Love clean, flexible, and business-ready automations.","verified":true,"links":["https://www.linkedin.com/in/abdallaelshikh0/"],"avatar":"https://gravatar.com/avatar/973e6dffba44e02742cd48ed057b82ed4434a725f67cef34bc3c50feac21c714?r=pg&d=retro&size=200"},"nodes":[{"id":20,"icon":"fa:map-signs","name":"n8n-nodes-base.if","codex":{"data":{"alias":["Router","Filter","Condition","Logic","Boolean","Branch"],"details":"The IF node can be used to implement binary conditional logic in your workflow. 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