by Hemanth Arety
Automatically fetch, curate, and distribute Reddit content digests using AI-powered filtering. This workflow monitors multiple subreddits, ranks posts by relevance, removes spam and duplicates, then delivers beautifully formatted digests to Telegram, Discord, or Slack. Who's it for Perfect for content creators tracking trends, marketers monitoring discussions, researchers following specific topics, and community managers staying informed. Anyone who wants high-quality Reddit updates without manually browsing multiple subreddits. How it works The workflow fetches top posts from your chosen subreddits using Reddit's JSON API (no authentication required). Posts are cleaned, deduplicated, and filtered by upvote threshold and custom keywords. An AI model (Google Gemini, OpenAI, or Claude) then ranks remaining posts by relevance, filters out low-quality content, and generates a formatted digest. The final output is delivered to your preferred messaging platform on a schedule or on-demand. Setup requirements n8n version 1.0+ AI provider API key (Google Gemini recommended - has free tier) At least one messaging platform configured: Telegram bot token + chat ID Discord webhook URL Slack OAuth token + channel access How to set up Open the Configuration node and edit subreddit list, post counts, and keywords Configure the Schedule Trigger or use manual execution Add your AI provider credentials in the AI Content Curator node Enable and configure your preferred delivery platform (Telegram/Discord/Slack) Test with manual execution, then activate the workflow Customization options Subreddits**: Add unlimited subreddits to monitor (comma-separated) Time filters**: Choose from hour, day, week, month, year, or all-time top posts Keywords**: Set focus keywords to prioritize and exclude keywords to filter out Post count**: Adjust how many posts to fetch vs. how many appear in final digest AI prompt**: Customize ranking criteria and output format in the AI node Schedule**: Use cron expressions for hourly, daily, or weekly digests Output format**: Modify the formatting code to match your brand style Add email notifications, database storage, or RSS feed generation by extending the workflow with additional nodes.
by Mark Ma
How it works This workflow is your personal CEO Brain. Every Saturday night, it automatically collects the past week’s activity across: 📩 Gmail: filters out spam, promos, receipts, etc. 📅 Google Calendar: grabs past week and upcoming month 🗒️ Notion Weekly Plan: pulls and analyzes a photo of your weekly plan (e.g., taken from a paper planner/notebook) using GPT-4o 🎯 Notion OKRs: fetches quarterly OKRs and formats them for summary It sends all the data to GPT-4.1, which generates a smart weekly report — including progress check, misalignments, overdue follow-ups, and next steps — then emails it to you as a Weekly OKR Report. Set up steps 🧠 Add your Gmail, Google Calendar, Notion, and OpenAI credentials 📅 The Notion Weekly Plan should have a date property and an image field that holds a photo of your planner/notebook page 🎯 The Notion OKR database should include objective, key result, and status fields 🔁 Schedule is preset to every Saturday at 20:30 HK time (cron 0 30 20 * * 6). Also set the workflow timezone in n8n and, if self-hosting, the server/container TZ (e.g., TZ=Asia/Hong_Kong) to ensure correct triggering 💬 You can modify the AI prompts inside the LLM nodes for more customized outputs
by Avkash Kakdiya
How it works This workflow starts whenever a new lead is submitted through Typeform. It cleans and stores the raw lead data, checks if the email is business-related (not Gmail), and then uses AI to enrich the lead with company details. After enrichment, the workflow scores the lead with AI, updates your HubSpot CRM, and saves everything neatly into Google Sheets for tracking and reporting. Step-by-step Capture New Lead Triggered by a new Typeform submission. Collects basic details: Name, Email, Phone, and Message. Saves raw lead data into a Google Sheet for backup. Stores the basic info in Airtable (avoids duplicates by email). Format & Filter Leads Formats the incoming data into a clean structure. Filters out non-business emails (e.g., Gmail) so only qualified leads continue. Enrich Company Information Uses AI (GPT-4o-mini) to enrich the lead’s company data based on email domain. Returns details like: Company Name, Industry, Headquarters, Employee Count, Website, LinkedIn, and Description. Merges this information with the original lead profile and adds metadata (timestamp, workflow ID). Score the Lead AI analyzes the enriched profile and assigns a lead score (1–10). Scoring considers industry fit, company size, contact source, and domain reputation. Update CRM & Sheets Sends the enriched lead with score to HubSpot CRM. Updates company details, contact info, and custom properties (lead_score, LinkedIn, description). Logs the fully enriched lead in a Google Sheet for tracking. Why use this? Automatically enriches and scores every incoming lead. Filters out low-value (non-business) emails before wasting CRM space. Keeps HubSpot CRM up to date with the latest company and contact info. Maintains both raw and enriched lead data in Google Sheets for easy reporting. Saves your team hours of manual research and ensures consistent, AI-driven lead qualification.
by Bilel Aroua
👥 Who is this for? Creators, marketers, and brands that want to turn a single product photo into premium motion clips, then optionally publish to Instagram/TikTok/YouTube via LATE. No editing skills required. ❓ What problem does it solve? Producing short vertical ads from a static packshot takes time (retouching, motion design, soundtrack, publishing). This workflow automates the entire process: image enhancement → cinematic motion → optional upscale → soundtrack → share. 🛠️ What this workflow does Collects a product photo via Telegram. Generates two refined edit prompts + two motion prompts using multi-agent Gemini orchestration. Creates two edited images with Fal.ai Gemini-Flash (image edit). Renders two 5s vertical videos with Kling (via fal.run queue). Auto-stitches them (FFmpeg API) and optionally upscales with Topaz. Generates a clean ambient soundtrack with MMAudio. Sends previews + final links back on Telegram. Optionally publishes to Instagram, TikTok, YouTube Shorts, and more via LATE. ⚡ Setup Telegram**: Bot token (Telegram node). Fal.ai**: HTTP Header Auth (Authorization: Bearer <FAL_API_KEY>) for Gemini-Flash edit, Kling queue, FFmpeg compose, Topaz upscale, and MMAudio. Google Gemini** (PaLM credential) for AI agents. ImgBB**: API key for uploading original/edited images. LATE: create an account at **getlate.dev and use your API key for publishing (optional). ▶️ How to use Start the workflow and DM your bot a clear product photo (jpg/jpeg/webp). Approve the two still concepts when prompted in Telegram. The orchestrator generates cinematic motion prompts and queues Kling renders. Receive two motion previews, then a stitched final (upscaled + soundtrack). Choose to auto-publish to Instagram/TikTok/YouTube via LATE (optional). 🎨 How to customize Art Direction** → tweak the “Art Director” system message (lighting, backgrounds, grading). Motion Flavor** → adjust the “Motion Designer” vocabulary for different camera moves/dynamics. Durations/Aspect** → default is 9:16, 5s; you can change Kling duration. Soundtrack** → edit the MMAudio prompt to reflect your brand’s sonic identity. Publishing** → enable/disable LATE targets; customize captions/hashtags. ✅ Prerequisites A Telegram bot created via @BotFather. A Fal.ai account + API key. An ImgBB account + API key. (Optional) a LATE account with connected social profiles — sign up at getlate.dev. 💡 Detailed technical notes, architecture, and step-by-step flow explanation are included as sticky notes inside this workflow. 🆘 Support If you need help setting up or customizing this workflow: 📧 Email: bilsimaging@gmail.com 🌐 Website: bilsimaging.com I can provide guidance, troubleshooting, or custom extra workflow adaptations.
by WeblineIndia
Portfolio Exposure Risk Summary Generator > n8n + Google Sheets + Gemini + Slack + Gmail This workflow automatically analyzes portfolio data from Google Sheets on a scheduled basis, calculates exposure metrics such as sector allocation and concentration and generates a professional risk summary using Google Gemini. The report is then sent via email, shared on Slack and logged for historical tracking. Quick Implementation Steps Connect your Google Sheets portfolio data Add Gemini API credentials Configure Gmail and Slack nodes Set schedule trigger timing Run once to verify output and logging What It Does This workflow performs a complete portfolio risk analysis pipeline. It starts by fetching portfolio data from a Google Sheet and validating whether the required data exists. If valid data is present, it calculates key exposure metrics such as total portfolio value, sector allocation and concentration levels using a Code node. These calculated metrics are then passed to an AI model (Google Gemini), which generates a concise and professional risk summary. The workflow ensures the output is structured properly before preparing a final report. The final output is distributed through multiple channels: an email report, a Slack notification and a log entry in Google Sheets. If no portfolio data is available, the workflow gracefully handles the scenario by generating a fallback report and logging it. Who It's For Financial analysts monitoring portfolio risk Investment advisors managing client portfolios Business owners tracking asset exposure Automation enthusiasts building financial workflows Requirements to Use This Workflow n8n account (cloud or self-hosted) Google Sheets with portfolio data Google Gemini API credentials Gmail account connected in n8n Slack workspace connected in n8n How It Works & Setup Guide Step 1: Schedule Trigger Configure the "Schedule Risk Review" node Default: Weekly run (Monday at 9 AM) Step 2: Connect Portfolio Data Use "Read Portfolio From Sheet" node Ensure sheet contains columns like: Asset Name Sector Quantity Price Market Value (optional) Step 3: Validate Data "Check Portfolio Data Exists" ensures valid entries exist If no data → fallback branch executes Step 4: Calculate Exposure Metrics Code node calculates: Total portfolio value Sector exposure and percentages Top holding concentration Top 3 holdings Step 5: Generate AI Risk Summary "Generate Risk Summary" uses Gemini model Input includes calculated metrics Output is structured JSON with risk summary Step 6: Parse AI Output "Parse Risk Summary" ensures valid JSON output Handles fallback parsing if needed Step 7: Prepare Final Report "Prepare Risk Report Data" formats final fields: Total value Top sector and exposure Top holding and concentration Risk summary Run date and ID Step 8: Deliver Report Email sent via Gmail node Slack message posted Data logged in Google Sheets Step 9: Fallback Handling If no portfolio data exists: Default summary is generated Logged to sheet for consistency How To Customize Nodes Modify schedule timing in Schedule node Update Google Sheet structure as per your data Adjust Code node logic for additional metrics Customize AI prompt for different analysis styles Change email/Slack message formatting Add‑ons Add risk scoring system (low/medium/high) Integrate historical trend analysis Add client-specific reporting Include asset-level risk breakdown Connect dashboards (e.g., Google Data Studio) Use Case Examples Weekly portfolio risk monitoring Client portfolio reporting for advisors Internal investment review automation Risk alerts for concentrated holdings Portfolio diversification analysis Troubleshooting Guide | Issue | Possible Cause | Solution | | --------------------------- | ------------------------- | ----------------------------------- | | No data processed | Empty Google Sheet | Ensure portfolio data exists | | AI output not parsed | Invalid JSON response | Check prompt format or parsing node | | Email not sent | Gmail credentials missing | Reconnect Gmail account | | Slack message not delivered | Incorrect channel setup | Verify Slack channel ID | | Incorrect calculations | Missing numeric values | Validate sheet data types | Need Help? If you need assistance setting up this workflow, customizing it for your business or building advanced automation solutions, feel free to reach out to our n8n automation developers at WeblineIndia / Global. Our team specializes in building scalable and production-ready automation workflows tailored to your needs. We can help you extend this workflow with advanced analytics, dashboards, integrations and enterprise-grade solutions.
by Will Carlson
What it does: Collects cybersecurity news from trusted RSS feeds and uses OpenAI’s Retrieval-Augmented Generation (RAG) capabilities with Pinecone to filter for content that is directly relevant to your organization’s tech stack. “Relevant” means the AI looks for news items that mention your specific tools, vendors, frameworks, cloud platforms, programming languages, operating systems, or security solutions — as described in your .txt scope documents. By training on these documents, the system understands the environment you operate in and can prioritize news that could affect your security posture, compliance, or operational stability. Once filtered, summaries of the most important items are sent to your work email every day. How it works Pulls in news from multiple cybersecurity-focused RSS feeds:** The workflow automatically collects articles from trusted, high-signal security news sources. These feeds cover threat intelligence, vulnerability disclosures, vendor advisories, and industry updates. Filters articles for recency and direct connection to your documented tech stack:** Using the publish date, it removes stale or outdated content. Then, leveraging your .txt scope documents stored in Pinecone, it checks each article for references to your technologies, vendors, platforms, or security tools. Uses OpenAI to generate and review concise summaries:** For each relevant article, OpenAI creates a short, clear summary of the key points. The AI also evaluates whether the article provides actionable or critical information before passing it through. Trains on your scope using Pinecone Vector Store (free) for context-aware filtering:** Your scope documents are embedded into a vector store so the AI can “remember” your environment. This context ensures the filtering process understands indirect or non-obvious connections to your tech stack. Aggregates and sends only the most critical items to your work email:** The system compiles the highest-priority news items into one daily digest, so you can review key developments without wading through irrelevant stories. What you need to do: Setup your OpenAI and Pinecone credentials in the workflow Create and configure a Pinecone index (dimension 1536 recommended) Pinecone is free to setup. Setup Pinecone with a single free index. Use a namespace like: scope. Make sure the embedding model is the same for all of your Pinecone references. Submit .txt scope documents listing your technologies, vendors, platforms, frameworks, and security products. .txt does not need to be structured. Add as much detail as possible. Update AI prompts to accurately describe your company’s environment and priorities.
by Harry Siggins
This n8n template automatically processes your industry newsletters and creates AI-powered intelligence briefs that filter signal from noise. Perfect for busy professionals who need to stay informed without information overload, it delivers structured insights directly to Slack while optionally saving content questions to Notion. Who's it for Busy executives, product managers, and content creators at growing companies who subscribe to multiple industry newsletters but lack time to read them all. Ideal for teams that need to spot trends, generate content ideas, and share curated insights without drowning in information. How it works The workflow runs daily to fetch labeled emails from Gmail, combines all newsletter content, and sends it to an AI agent for intelligent analysis. The AI filters developments through your specific business lens, identifies only operationally relevant insights, and generates thought-provoking questions for content creation. Results are formatted as rich Slack messages using Block Kit, with optional Notion integration for tracking content ideas. Requirements Gmail account with newsletter labeling system OpenRouter API key for AI analysis (costs approximately $0.01-0.05 per run) or API key for a specific LLM Slack workspace with bot permissions for message posting Notion account with database setup (optional, for content question tracking) Perplexity API key (optional, for additional AI research capabilities) How to set up 1 Connect your Gmail, OpenRouter, and Slack credentials through n8n's secure credential system. Create a Gmail label for newsletters you want analyzed and setup in the "Get Labeled Newsletters" node. Update the Slack channel ID in the "Send to Slack" node. The template comes pre-configured with sample settings for tech companies, so you can run it immediately after credential setup. How to customize the workflow Edit the "Configuration" node to match your industry and audience - change the 13 pre-defined fields including target audience, business context, relevance filters, and content pillars. Adjust the cron expression in the trigger node for your timezone. Modify the Slack formatting code to change output appearance, or add additional destination nodes for email, Teams, or Discord. Remove Notion nodes if you only need Slack output. The AI analysis framework is fully customizable through the Configuration node, allowing you to adapt from the default tech company focus to any industry including healthcare, finance, marketing, or consulting.
by Rahul Joshi
Description Automates daily EOD summaries from Jira issues into an Excel sheet, then compiles a weekly summary using Azure OpenAI (GPT-4o-mini) and delivers it to stakeholders via email. Gain consistent reporting, clear insights, and hands-free delivery. ✨📧 What This Template Does Fetches Jira issues and extracts key fields. 🧩 Generates End‑of‑Day summaries and stores them in Excel daily. 📄 Aggregates the week’s EOD data from Excel. 📚 Creates a weekly summary using Azure OpenAI (GPT-4o-mini). 🤖 Delivers the weekly report to stakeholders via email. 📬 Key Benefits Saves time with fully automated daily and weekly reporting. ⏱️ Ensures consistent, structured summaries every time. 📏 Improves clarity for stakeholders with readable insights. 🪄 Produces mobile-friendly email summaries for quick consumption. 📱 No-code customization inside n8n. 🛠 Features Jira issue ingestion and transformation. Daily EOD summary generation and Excel storage. Weekly AI summarization with Azure OpenAI (GPT-4o-mini). Styled HTML email output to stakeholders. Scheduling for hands-free execution. Requirements An n8n instance (cloud or self-hosted). Jira access to read issues. Azure OpenAI (GPT-4o-mini) for weekly AI summarization. Email service (Gmail/SMTP) configured in n8n credentials. Excel/Sheet storage set up to append and read daily EOD entries. Target Audience Engineering and product teams needing routine summaries. Project managers tracking daily progress. Operations teams consolidating weekly reporting. Stakeholders who prefer clean email digests. Step-by-Step Setup Instructions Jira: Connect your Jira credentials and confirm issue read access. Azure OpenAI: Deploy GPT-4o-mini and add Azure OpenAI credentials in n8n. Gmail/SMTP: Connect your email account in n8n Credentials and authorize sending. Excel/Sheet: Configure the sheet used to store daily EOD summaries. Import the workflow, assign credentials to nodes, replace placeholders, then run and schedule. Security Best Practices Use scoped API tokens for Jira with read-only permissions. 🔐 Store Azure OpenAI and email credentials in n8n’s encrypted credentials manager. 🧯 Limit email recipients to approved stakeholder lists. 🚦 Review logs regularly and rotate credentials on a schedule. ♻
by Rahul Joshi
Description Automatically generate polished, n8n-ready template descriptions from your saved JSON workflows in Google Drive. This AI-powered automation processes workflow files, drafts compliant descriptions, and delivers Markdown and HTML outputs directly to your inbox. 🚀💌📊💬 What This Template Does Manually triggers the workflow to start processing. Searches a specified Google Drive folder for JSON workflow files. Iterates through each JSON file found in that folder. Downloads each file and prepares it for data extraction. Parses workflow data from the downloaded JSON content. Uses Azure OpenAI GPT-4 to generate concise titles and detailed descriptions. Converts the AI output into structured Markdown for n8n template publishing. Creates an HTML version of the description for email delivery. Logs generated details into a Google Sheet for record-keeping. Sends an email containing the Markdown and HTML descriptions to the target recipient. Key Benefits ✅ Fully automates n8n template description creation. ✅ Ensures consistency with official n8n publishing guidelines. ✅ Saves time while eliminating human writing errors. ✅ Provides dual Markdown + HTML outputs for flexibility. ✅ Centralizes workflow metadata in Google Sheets. ✅ Simplifies collaboration and version tracking via email delivery. Features Manual workflow trigger for controlled execution. Integration with Google Drive for locating and downloading JSON files. Intelligent parsing of workflow data from JSON structure. GPT-4-powered AI for title and description generation. Automatic Markdown + HTML formatting for n8n publishing. Google Sheets integration for persistent record-keeping. Automated Gmail delivery of generated documentation. Requirements n8n instance (cloud or self-hosted). Google Drive OAuth2 credentials with file read permissions. Google Sheets OAuth2 credentials with edit permissions. Azure OpenAI GPT-4 API key for AI text generation. Gmail OAuth2 credentials for email sending. Target Audience n8n content creators documenting workflows. 👩💼 Automation teams handling multiple template deployments. 🔄 Agencies and freelancers managing workflow documentation. 🏢 Developers leveraging AI for faster template creation. 🌐 Technical writers ensuring polished, standardized outputs. 📊 Step-by-Step Setup Instructions Connect your Google Drive account and specify the folder containing JSON workflows. 🔑 Authorize Google Sheets and confirm access to the tracking spreadsheet. ⚙️ Add Azure OpenAI GPT-4 API credentials for AI-powered text generation. 🧠 Connect Gmail credentials for automated email delivery. 📧 Run the workflow manually using a test JSON file to validate all nodes. ✅ Enable the workflow to automatically generate and send descriptions as needed. 🚀
by Felix Kemeth
Overview Staying up to date with fast-moving topics like AI, machine learning, or your specific industry can be overwhelming. You either drown in daily noise or miss important developments between weekly digests. This AI News Agent workflow delivers a curated newsletter only when there's genuinely relevant news. I use it myself for AI and n8n topics. Key features: AI-driven send decision**: An AI agent evaluates whether today's news is worth sending. Deduplication**: Compares candidate articles against past newsletters to avoid repetition. Real-time news**: Uses SerpAPI's DuckDuckGo News engine for fresh results. Frequency guardrails**: Configure minimum and maximum days between newsletters. In this post, I'll walk you through the complete workflow, explain each component, and show you how to set it up yourself. What this workflow does At a high level, the AI News Agent: Fetches fresh news twice daily via SerpAPI's DuckDuckGo News engine. Stores articles in a persistent data table with automatic deduplication. Filters for freshness - only considers articles newer than your last newsletter. Applies frequency guardrails - respects your min/max sending preferences. Makes an editorial decision - AI evaluates if the news is worth sending. Enriches selected articles - uses Tavily web search for fact-checking and depth. Delivers via Telegram - sends a clean, formatted newsletter. Remembers what it sent - stores each edition to prevent future repetition. This allows you to get newsletters only when there's genuinely relevant news - in contrast to a fixed schedule. Requirements To run this workflow, you need: SerpAPI key** Create an account at serpapi.com and generate an API key. They offer 250 free searches/month. Tavily API key** Sign up at app.tavily.com and create an API key. Generous free tier available. OpenAI API key** Get one from OpenAI - required for AI agent calls. Telegram bot + chat ID** A free Telegram bot (via BotFather) and the chat/channel ID where you want the newsletter. See Telegram's bot tutorial for setup. How it works The workflow is organized into five logical stages. Stage 1: Schedule & Configuration Schedule Trigger** Runs the workflow on a cron schedule. Default: 0 0 9,17 * * * (twice daily at 9:00 and 17:00). These frequent checks enable the AI to send newsletters at these times when it observes actually relevant news, not only once a week. I picked 09:00 and 17:00 as natural check‑in points at the start and end of a typical workday, so you see updates when you’re most likely to read them without being interrupted in the middle of deep work. With SerpAPI’s 250 free searches/month, running twice per day with a small set of topics (e.g. 2–3) keeps you comfortably below the limit; if you add more topics or increase the schedule frequency, either tighten the cron window or move to a paid SerpAPI plan to avoid hitting the cap. Set topics and language** A Set node that defines your configuration: topics: comma-separated list (e.g., AI, n8n) language: output language (e.g., English) minDaysBetween: minimum days to wait (0 = no minimum) maxDaysBetween: maximum days without sending (triggers a "must-send" fallback) Stage 2: Fetch & Store News Build topic queries** Splits your comma-separated topics into individual search queries: In DuckDuckGo News via SerpAPI, a query like AI,n8n looks for news where both “AI” and “n8n” appear. For a niche tool like n8n, this is often almost identical to just searching for n8n (docs). It’s therefore better to split the topics, search for each of them separately, and let the AI later decide which news articles to select. return $input.first().json.topics.split(',').map(topic => ({ json: { topic: topic.trim() } })); Fetch news from SerpAPI (DuckDuckGo News)** HTTP Request node calling SerpAPI with: engine: duckduckgo_news q: your topic df: d (last day) Auth is handled via httpQueryAuth credentials with your SerpAPI key. SerpAPI also offers other news engines such as the Google News API (see here). DuckDuckGo News is used here because, unlike Google News, it returns an excerpt/snippet in addition to the title, source, and URL (see here)—giving the AI more context to work with. _Another option is NewsAPI, but its free tier delays articles by 24 hours, so you miss the freshness window that makes these twice-daily checks valuable. DuckDuckGo News through SerpAPI keeps the workflow real-time without that lag._ n8n has official SerpAPI nodes, but as of writing there is no dedicated node for the DuckDuckGo News API. That’s why this workflow uses a custom HTTP Request node instead, which works the same under the hood while giving you full control over the DuckDuckGo News parameters. Split SerpAPI results into articles** Expands the results array so each article becomes its own item. Upsert articles into News table** Stores each article in an n8n data table with fields: title, source, url, excerpt, date. Uses upsert on title + URL to avoid duplicates. Date is normalized to ISO UTC: DateTime.fromSeconds(Number($json.date), {zone: 'utc'}).toISO() Stage 3: Filtering & Frequency Guardrails This is where the workflow gets smart about what to consider and when to send. Get previous newsletters → Sort → Get most recent** Pulls all editions from the Newsletters table and isolates the latest one with its createdAt timestamp. Combine articles with last newsletter metadata** Attaches the last newsletter timestamp to each candidate article. Filter articles newer than last newsletter** Keeps only articles published after the last edition. Uses a safe default date (2024-01-01) if no previous newsletter exists: $json.date_2 > ($json.createdAt_1 || DateTime.fromISO('2024-01-01T00:00:00.000Z')) Stop if last newsletter is too recent** Compares createdAt against your minDaysBetween setting. If you're still in the "too soon to send" window, the workflow short-circuits here. Stage 4: AI Editorial Decision This is the core intelligence of the workflow - an AI that decides whether to send and what to include. This stage is also the actual agentic part of the workflow, where the system makes its own decisions instead of just following a fixed schedule. Aggregate candidate articles for AI** Bundles today's filtered articles into a compact list with title, excerpt, source, and url. Limit previous newsletters to last 5 → Aggregate** Prepares the last 5 newsletter contents for the AI to check against for repetition. Combine candidate articles with past newsletters** Merges both lists so the AI sees "today's candidates" + "recent history" side by side. AI: decide send + select articles** The heart of the workflow. A GPT-5.1 call with a comprehensive editorial prompt: You are an AI Newsletter Editor. Your job is to decide whether today’s newsletter edition should be sent, and to select the best articles. You will receive a list of articles with: 'title', 'excerpt', source, url. You will also receive content of previously sent newsletters (markdown). Your Tasks 1. Decide whether to send the newsletter Output "YES" only if all of the following are satisfied OR the fallback rule applies: Base Criteria There are at least 3 meaningful articles. Meaningful = not trivial, not purely promotional, not clickbait, contains actual informational value. Articles must be non-duplicate and non-overlapping: Not the same topic/headline rephrased Not reporting identical events with minor variations Not the same news covered by multiple sources without distinct insights Articles must be relevant to the user's topics: {{ $('Set topics and language').item.json.topics }} Articles must be novel relative to the topics in previous newsletters: Compare against all previous newsletters below Exclude articles that discuss topics already substantially covered Articles must offer clear value: New information Impact that matters to the user Insight, analysis, or meaningful expansion Fallback rule: Newsletter frequency requirement If at least 1 relevant article exists and the last newsletter was sent more than {{ $('Set topics and language').item.json.maxDaysBetween }} days ago, then you MUST return "YES" as a decision even if the other criteria are not completely met. Last newsletter was sent {{ $('Get most recent newsletter').item.json.createdAt ? Math.floor($now.diff(DateTime.fromISO($('Get most recent newsletter').item.json.createdAt), 'days').days) : 999 }} days ago. Otherwise → "NO" 2. If "YES": Select Articles Select the top 3–5 articles that best fulfill the criteria above. For each selected article, output: title** (rewrite for clarity, conciseness, and impact) summary** (1–2 sentences; written in the output language) source** url** All summaries must be written in: {{ $('Set topics and language').item.json.language }} Output Format (JSON) { "decision": "YES or NO", "articles": [ { "title": "...", "summary": "...", "source": "...", "url": "..." } ] } When "decision": "NO", return an empty array for "articles". Article Input Use these articles: {{ $json.results.map( article => `Title: ${article.title_2} Excerpt: ${article.excerpt_2} Source: ${article.source_2} URL: ${article.url_2}` ).join('\n---\n') }} You must also consider the topics already covered in previous newsletters to avoid repetition: {{ $json.newsletters.map(x => Newsletter: ${x.content}).join('\n---\n') }} The AI outputs structured JSON: { "decision": "YES", "articles": [ { "title": "...", "summary": "...", "source": "...", "url": "..." } ] } If AI decided to send newsletter** Routes based on decision === "YES". If NO, the workflow ends gracefully. Stage 5: Content Enrichment & Delivery Split selected articles for enrichment** Each selected article becomes its own item for individual processing. AI: enrich & write article** An AI Agent node with GPT-5.1 + Tavily web search tool. For each article: You are a research writer that updates short news summaries into concise, factual articles. Input: Title: {{ $json["title"] }} Summary: {{ $json["summary"] }} Source: {{ $json["source"] }} Original URL: {{ $json["url"] }} Language: {{ $('Set topics and language').item.json.language }} Instructions: Use Tavily Search to gather 2–3 reliable, recent, and relevant sources on this topic. Update the title if a more accurate or engaging one exists. Write 1–2 sentences summarizing the topic, combining the original summary and information from the new sources. Return the original source name and url as well. Output (JSON): { "title": "final article title", "content": "concise 1–2 sentence article content", "source": "the name of the original source", "url": "the url of the original source" } Rules: Ensure the topic is relevant, informative, and timely. Translate the article if necessary to comply with the desired language {{ $('Set topics and language').item.json.language }}. The Output Parser enforces the JSON schema with title, content, source, and url fields. Aggregate enriched articles** Collects all enriched articles back into a single array. Insert newsletter content into Newsletters table** Stores the final markdown content for future deduplication: $json.output.map(article => { const title = JSON.stringify(article.title).slice(1, -1); const content = JSON.stringify(article.content).slice(1, -1); const source = JSON.stringify(article.source).slice(1, -1); const url = JSON.stringify(article.url).slice(1, -1); return ${title}\n${content}\nSource: ${source}; }).join('\n\n') Send newsletter to Telegram** Sends the formatted newsletter to your Telegram chat/channel. Why this workflow is powerful Intelligent send decisions** The AI evaluates news quality before sending, leading to a less noisy and more relevant news digest. Memory across editions** By persisting newsletters and comparing against history, the workflow avoids repetition. Frequency guardrails with flexibility** Set boundaries (e.g., "at least 1 day between sends" and "must send within 5 days"), but let the AI decide the optimal moment within those bounds. Source-level deduplication** The news table with upsert prevents the same article from being considered multiple times across runs. Grounded in facts** SerpAPI provides real news sources; Tavily enriches with additional verification. The newsletter stays factual. Configurable and extensible** Change topics, language, frequency - all in one Set node. In addition, the workflow is modular, allowing to add new news sources or new delivery channels without touching the core logic. Configuration guide To customize this workflow for your needs: Topics and language Open Set topics and language and modify: topics: your interests (e.g., machine learning, startups, TypeScript) language: your preferred output language Frequency settings minDaysBetween: minimum days between newsletters (0 = no limit) maxDaysBetween: maximum gap before forcing a send For very high-volume topics (such as "AI"), expect the workflow to send almost every time once minDaysBetween has passed, because the content-quality criteria are usually met. Schedule Modify the Schedule Trigger cron expression. Default runs twice daily at 9:00 am and 5:00 pm; adjust to your preference. Telegram Update the chatId in the Telegram node to your chat/channel. Credentials Set up credentials for: SerpAPI (httpQueryAuth), Tavily, OpenAI, Telegram. Next steps and improvements Here are concrete directions to take this workflow further: Multi-agent architecture** Split the current AI calls into specialized agents: signal detection, relevance scoring, editorial decision, content enhancement, and formatting - each with a single responsibility. 1:1 personalization** Move from static topics to weighted preferences. Learn from click behavior and feedback. Telegram feedback buttons** Add inline buttons (👍 Useful / 👎 Not relevant / 🔎 More like this) and feed signals back into ranking. Email with HTML template** For more flexibility, send the newsletter via email. Incorporating other news APIs or RSS feeds** Add more sources such as other news APIs and RSS feeds from blogs, newsletters, or communities. Adjust for arxiv paper search and research news** Swap SerpAPI for arxiv search or other academic sources to obtain a personal research digest newsletter. Images and thumbnails** Fetch representative images for each article and include them in the newsletter. Web archive** Auto-publish each edition as a web page with permalinks. Retry logic and error handling** Add exponential backoff for external APIs and route failures to an error workflow. Prompt versioning** Move prompts to a data table with versioning for A/B testing and rollback. Audio and video news** Use audio or video models for better news communication. Wrap-up This AI News Agent workflow represents a significant evolution from simple scheduled newsletters. By adding intelligent send decisions, historical deduplication, and frequency guardrails, you get a newsletter that respects the quality of available news. I use this workflow myself to stay informed on AI and automation topics without the overload of daily news or the delayed delivery caused by a fixed newsletter schedule. Need help with your automations? Contact me here.
by Ruth Olatunji
Eliminate 90% of manual work in procurement by automating quote requests, response tracking, price extraction, and supplier follow-ups. This complete automation handles everything from sending personalized emails to extracting pricing data with AI and sending WhatsApp reminders—so you can focus on decision-making, not data entry. This all-in-one workflow transforms a 5-hour manual process into a 10-minute review task, saving 15-20 hours per month while improving supplier response rates by 30%. How it works This workflow contains 4 independent automation modules running on separate schedules: Quote Request Sender (Manual trigger) Reads supplier list from Google Sheets Sends personalized emails via Gmail with category and deadline Logs all requests with timestamps to tracking sheet Response Monitor (Hourly schedule) Automatically checks Gmail for supplier replies with attachments Updates tracking sheet status to "Quote Received" Zero manual email monitoring required AI Price Extraction (Manual trigger) Downloads PDF/Excel attachments from emails Extracts text using n8n's built-in parser Sends to OpenAI GPT-4o-mini to identify products, prices, quantities, currencies Saves structured data to Price Comparison sheet WhatsApp Follow-ups (Daily at 9 AM) Checks for non-responsive suppliers Sends smart reminders at Day 3, 5, and 7 with escalating urgency Falls back to email if no phone number Logs all follow-up history Each module shares data through Google Sheets while running independently. Set up steps Time to set up: 20-30 minutes Create two Google Sheets: "Quote Tracking" (with columns: supplier_name, supplier_email, category, request_date, status, quote_received, phone_number, last_follow_up, follow_up_count) and "Price Comparison" (with columns: supplier_name, supplier_email, product_name, price, currency, quantity, extracted_date, source_file) Connect credentials: Gmail OAuth, Google Sheets OAuth (same account), OpenAI API key, Twilio Account SID + Auth Token Update all Google Sheet IDs in every Google Sheets node (8 nodes total across all modules) Configure Twilio WhatsApp sandbox: Go to Twilio Console → Messaging → WhatsApp → Send join code from your phone → Update "From" number in Send WhatsApp node Add 2-3 test suppliers to Tracking Sheet with your email addresses using + trick (yourname+supplier1@gmail.com) and phone numbers in international format Test each module: Execute Quote Sender → Reply to test email with PDF → Execute AI Extraction → Set supplier date to 3 days ago → Test Follow-ups Activate schedules for Response Monitor (hourly) and Follow-ups (daily at 9 AM) Detailed node configurations and troubleshooting tips are included in sticky notes throughout the workflow canvas. Requirements Gmail account with API access Google Sheets (2 sheets) OpenAI API account (~$5-15/month) Twilio account with WhatsApp (~$10-20/month) n8n (any version supporting HTTP Request node) Who is this for Procurement teams managing multiple supplier quotes Small businesses comparing vendor prices Operations managers handling RFQs Purchasing departments drowning in email attachments Anyone collecting and tracking supplier pricing at scale Time savings: From 5 hours to 10 minutes per quote cycle (90% reduction) Response rate improvement: 50% → 80% with automated follow-ups Accuracy: 95%+ AI extraction accuracy vs 5-10% manual data entry errors
by Avkash Kakdiya
How it works This workflow automatically generates an AI-powered revenue forecast whenever a new deal is created in HubSpot. It collects all active deals, standardizes key sales data, and sends it to an AI model for forecasting and risk analysis. The AI produces best, likely, and worst-case revenue scenarios along with actionable insights. Results are shared with stakeholders via Slack and Email and stored in Google Sheets for tracking. Step-by-step Step 1 : Collect & prepare HubSpot deals** HubSpot Trigger – Starts the workflow when a new deal is created in HubSpot. Get many deals – Fetches all active deals from the sales pipeline. Format HubSpot Data – Cleans and standardizes deal fields like amount, stage, probability, and region. Loop Over Items – Iterates through formatted deals to prepare them for AI analysis. Step 2 : Generate & distribute AI forecast** AI Revenue Forecast & Risk Analysis – Sends pipeline data to the AI model to generate revenue forecasts and insights. Groq Chat Model – Powers the AI analysis and produces structured forecasting output. Format AI response – Extracts key metrics, risks, and recommendations from the AI response. Send a message (Gmail) – Emails the revenue forecast report to stakeholders. Send a message (Slack) – Posts the forecast summary to a selected Slack channel. Append row in sheet – Logs forecast data and insights into Google Sheets. Wait – Adds a controlled pause before looping or completing the workflow. Why use this? Get real-time revenue forecasts triggered directly by CRM activity. Reduce manual pipeline analysis and reporting effort. Identify high-risk deals early with AI-driven insights. Keep leadership aligned through automated Slack and Email updates. Maintain a historical forecast log for audits and performance tracking.