Workflow Templates

How it works This workflow monitors incoming Gmail messages for refund requests and uses AI to extract the order ID and reason. It then retrieves order details from Shopify to evaluate refund eligibility. Based on the order status, it automatically approves or routes the request for manual review. Customers and internal teams are notified accordingly, and all actions are logged in Google Sheets. Step-by-step Capture refund requests from email** Gmail Trigger – Detects unread emails with “Refund Request” in the subject. AI Agent2 – Extracts order ID and reason using AI. Groq Chat Model – Provides the language model used by the AI agent. Code in JavaScript – Converts AI output into structured JSON. Fetch order details from Shopify** Get an order – Retrieves order information using the extracted order ID. Evaluate refund eligibility** If – Checks whether the order status is “Delivered” to allow auto-approval. Handle responses and logging** Send a message to customer – Sends approval confirmation if eligible. Send a message to the team – Notifies team for manual review cases. Send a message to customer for "Pending" Status – Updates customer about manual review. Logs to Sheet – Records all refund actions and details in Google Sheets. Why use this? Automates repetitive refund handling tasks and reduces workload Improves customer experience with faster response times Ensures consistent decision-making based on order status Keeps a centralized log for auditing and reporting Easily extendable with additional validation rules or integrations

How it works This workflow automatically fetches the latest public grant information from the Ministry of Health, Labour and Welfare (MHLW) RSS feed. It uses AI to summarize and structure each grant post into a clear format, stores the results in Google Sheets, and sends a formatted HTML summary via Gmail. Workflow summary Schedule Trigger – Runs the flow daily or weekly. RSS Feed Reader – Fetches the latest MHLW news and updates. Text Classifier (AI) – Categorizes the item as “Grant/Subsidy”, “Labor-related”, or “Other”. AI Agent – Extracts structured data such as title, summary, deadline, amount, target, and URL. Google Sheets – Appends or updates the database using the grant title as the key. Code Node – Builds an HTML report summarizing new entries. Gmail – Sends a daily digest email to your inbox. Setup steps Add your OpenRouter API key as a credential (used in the AI Agent). Replace the Google Sheets ID and sheet name with your own. Update the recipient email address in the Gmail node. Adjust the schedule trigger to match your preferred frequency. (Optional) Add more RSS feeds if you want to monitor other sources. Ideal for Consultants or administrators tracking subsidy and grant programs Small business owners who want automatic updates Anyone who wants a daily AI-summarized government grant digest ⚙️ Note: Detailed explanations and setup hints are included as Sticky Notes above each node inside the workflow.

Description Automatically consolidate Zendesk and Freshdesk ticket data into a unified performance dashboard with KPI calculations, Google Sheets logging, real-time Slack alerts, and weekly Gmail email reports. Provides complete visibility into support operations, SLA compliance, and customer satisfaction across multiple platforms. 📊💬📧 What This Template Does Runs weekly on schedule to fetch tickets from both Zendesk and Freshdesk. ⏰ Merges ticket data into a standardized JSON structure with normalized priorities, statuses, and channels. 🔄 Logs all tickets and metadata into Google Sheets for audit-ready performance tracking. 📑 Calculates advanced KPIs including resolution rates, SLA breaches, CSAT score estimation, urgent ticket rates, and performance grading. 📊 Evaluates alert conditions (e.g., high SLA breach, low CSAT, backlog risk). 🚨 Sends formatted Slack alerts with performance grades, key metrics, and recommendations. 💬 Generates corporate-style HTML weekly reports and delivers them via Gmail. 📧 Key Benefits Unifies Zendesk and Freshdesk data into one consistent reporting flow. 🌐 Provides actionable KPIs for SLA monitoring, customer satisfaction, and backlog health. ⏱️ Ensures leadership visibility with Google Sheets logs and professional email reports. 🧾 Alerts the support team instantly on Slack when performance drops. 🚨 Reduces manual data analysis with automated grading and recommendations. 🤖 Features Multi-Platform Ticket Integration – Fetches tickets from Zendesk and Freshdesk. 🎫 Data Normalization – Cleans descriptions, maps priorities/statuses, and detects escalations. 🧼 Google Sheets Logging – Tracks tickets with IDs, URLs, tags, timestamps, and metadata. 📈 KPI Calculation Engine – Computes SLA breach rate, resolution rate, CSAT, escalation %, and more. 🧮 Performance Grading – Grades support performance (A–D) with detailed descriptions. 🏅 Slack Alerts – Notifies with active alerts, recommendations, and emoji-based health signals. 📢 Weekly Gmail Reports – Delivers branded HTML reports for management and audits. ✨ Requirements n8n instance (cloud or self-hosted). Zendesk API credentials with ticket read access. Freshdesk API credentials with ticket read access. Google Sheets OAuth2 credentials with spreadsheet write permissions. Slack Bot API credentials with posting permissions. Gmail OAuth2 credentials with send email permissions. Pre-configured Google Sheet for KPI logging. Target Audience Support managers overseeing multi-platform ticketing systems. 👩‍💻 Customer success teams monitoring SLA compliance and CSAT health. 🚀 SMBs running Zendesk + Freshdesk who need unified dashboards. 🏢 Remote/global support teams needing automated KPI visibility. 🌐 Executives requiring weekly performance reports and recommendations. 📈 Step-by-Step Setup Instructions Connect Zendesk, Freshdesk, Google Sheets, Slack, and Gmail credentials in n8n. 🔑 Update the Google Sheet ID in the “Log KPIs in Google Sheets” node. 📊 Configure Slack channel ID for alerts (default: zendesk-churn-alerts). 💬 Replace {Enter Your Email} in the Gmail node with your recipient email. 📧 Adjust thresholds in the KPI calculation node (default: 4h response, 24h resolution). ⏱️ Test with sample tickets to validate Sheets logging, Slack alerts, and Gmail reports. ✅ Deploy on schedule (default: weekly at 8 PM) for continuous tracking. 🗓️

🧾An intelligent automation system that turns WhatsApp into your personal receipt manager — integrating Meta WhatsApp Cloud API, Google Drive, Google Sheets, and OpenAI GPT-4o-mini via n8n. 🎥 Demo: Watch the Loom walkthrough ⚙️ What It Does The AI-Powered WhatsApp Receipt Bot automates the complete invoice handling process through a conversational interface. Workflow Summary: User sends a receipt image via WhatsApp. The bot automatically downloads the media using the WhatsApp Cloud API. The image is uploaded to a Google Drive “Invoices” folder. The file is shared publicly, generating a shareable URL. The receipt is analyzed using OpenAI GPT-4o-mini to extract structured data: Store name Items purchased Payment method Total amount The extracted details are appended to a Google Sheet for record-keeping. The bot sends a human-readable summary back to WhatsApp with emojis and the invoice link. Output Example: 🏬 Store: Big Bazaar 📝 Items: Rice, Detergent, Snacks 💳 Payment: Card 💰 Total: ₹1520.75 🔗 Link: https://drive.google.com/file/d/1abcXYZ/view This system eliminates manual expense tracking, improves accuracy through OCR, and provides a seamless way to manage receipts in real time. 💡 Use Cases | Scenario | Description | | ------------------------------------- | --------------------------------------------------------------------------------------------------------------------- | | Personal Expense Management | Automatically store and categorize receipts from daily purchases. | | Business Accounting | Collect employee expense receipts through WhatsApp and centralize them in Google Sheets. | | Freelancer or Consultant Tracking | Keep a digital record of client reimbursements or software purchase receipts. | | Family Budgeting | Family members send receipts to one shared WhatsApp number, all data gets logged centrally. | | E-commerce / Delivery Teams | Drivers or delivery agents send invoices from the field to WhatsApp; data automatically goes to the accounting sheet. | 🔧 Setup 1. Accounts and Tools Needed | Tool | Purpose | Link | | -------------------------- | ------------------------------------------- | -------------------------------------------------------------------------------------------- | | Meta Developer Account | To access WhatsApp Business Cloud API | https://developers.facebook.com/apps | | Google Cloud Account | For enabling Drive and Sheets APIs | https://console.cloud.google.com | | n8n Instance | Workflow automation engine (local or cloud) | https://app.n8n.cloud | | OpenAI API Key | For GPT-4o-mini model OCR + reasoning | https://platform.openai.com/account/api-keys | 2. Meta Developer Setup (WhatsApp Cloud API) Go to Meta Developer Dashboard → My Apps → Create App → Business type. Add WhatsApp product under your app. Retrieve the following from WhatsApp > Configuration: Permanent Access Token Phone Number ID WhatsApp Business Account ID Add these credentials in n8n → Credentials → WhatsApp API. Use the same credentials for WhatsApp Trigger and Send Message nodes. Verify webhook in Meta with your n8n webhook URL. Important: In your HTTP Node, set the header as: Authorization: Bearer <access_token> Replace <access_token> with your WhatsApp Cloud API permanent token. Without this, the workflow will fail to send or receive WhatsApp messages properly. 3. Google Drive Setup Create a folder named Invoices on your Google Drive. Copy the Folder ID (found in the Drive URL). In Google Cloud Console → APIs & Services → Enable APIs: Enable Google Drive API Enable Google Sheets API Go to Credentials → Create Credentials → OAuth 2.0 Client ID. Download the credentials.json file. Upload this to n8n → Credentials → Google Drive OAuth2 API. Authorize the connection on first workflow run. 4. Google Sheets Setup Create a new Google Sheet titled Invoices. Add the following headers in Row 1: store name | discription | image_url | payment | total Copy the Sheet ID (from the URL). Add the ID under the Google Sheets Append node in n8n. Map each field to its corresponding value extracted from the OCR result. 5. OpenAI Setup Generate an API key from https://platform.openai.com/account/api-keys. Add it to n8n → Credentials → OpenAI API. Use model gpt-4o-mini in the “Analyze Image” node. Can upgrade to gpt-4o for better OCR accuracy if account supports it. 6. n8n Workflow Setup Import the provided n8n workflow JSON. Configure credentials for: WhatsApp API Google Drive OAuth2 Google Sheets OAuth2 OpenAI API Activate workflow and set webhook in Meta Developer console. Send a test receipt image to your WhatsApp Business number. The bot will automatically: Download → Upload → Extract → Log → Summarize → Reply 📊 Example Google Sheet Record | store name | discription | image_url | payment | total | | ---------- | ----------------------- | -------------------------------------------------------------------------------------------- | ------- | ------- | | Big Bazaar | Rice, Detergent, Snacks | https://drive.google.com/file/d/1abcXYZ/view | Card | 1520.75 | 🧠 Result A fully automated AI pipeline that transforms WhatsApp into a smart expense-tracking interface — integrating vision, automation, and natural language processing for zero-manual financial documentation. Support & Contact: If you face any issues during setup or execution, contact: 📧 Email: atharvapj5@gmail.com 🔗 LinkedIn: Atharva Jaiswal

ETL Monitoring & Alert Automation: Jira & Slack Integration This workflow automatically processes ETL errors, extracts important details, generates a preview, creates a log URL, classifies the issue using AI and saves the processed data into Google Sheets. If the issue is important or needs attention, it also creates a Jira ticket automatically. The workflow reduces manual debugging effort, improves visibility and ensures high-severity issues are escalated instantly without human intervention. Quick Start – Implementation Steps Connect your webhook or ETL platform to trigger the workflow. Add your OpenAI, Google Sheets and Jira credentials. Enable the workflow. Send a sample error to verify Sheets logging and Jira ticket creation. Deploy and let the workflow monitor ETL pipelines automatically. What It Does This workflow handles ETL errors end-to-end by: Extracting key information from ETL error logs. Creating a short preview for quick understanding. Generating a URL to open the full context log. Asking AI to identify root cause and severity. Parsing the AI output into clean fields. Saving the processed error to Google Sheets. Creating a Jira ticket for medium/high-severity issues. This creates a complete automated system for error tracking, analysis and escalation. Who’s It For DevOps & engineering teams monitoring data pipelines. ETL developers who want automated error reporting. QA teams verifying daily pipeline jobs. Companies using Jira for issue tracking. Teams needing visibility into ETL failures without manual log inspection. Requirements to Use This Workflow n8n account or self-hosted instance. ETL platform capable of sending error payloads (via webhook). OpenAI API Key. Google Sheets credentials. Jira Cloud API credentials. Optional: log storage URL (S3, Supabase, server logs). How It Works & Setup Steps 1. Get ETL Error (Webhook Trigger) Receives ETL error payload and starts the workflow. 2. Prepare ETL Logs (Code Node) Extracts important fields and makes a clean version of the error.Generates a direct link to open the full ETL log. 3. AI Severity Classification (OpenAI / AI Agent) AI analyzes the issue, identifies cause and assigns severity. 4. Parse AI Output (Code Node) Formats AI results into clean fields: severity, cause, summary, recommended action. 5. Prepare Data for Logging (Set / Edit Fields) Combines all extracted info into one final structured record. 6. Save ETL Logs (Google Sheets Node) Logs each processed ETL error in a spreadsheet for tracking. 7. Create Jira Ticket (Jira Node) Automatically creates a Jira issue when severity is Medium, High or Critical. 8. ETL Failure Alert (Slack Node) Sends a Slack message to notify the team about the issue. 9. ETL Failure Notify (Gmail Node) Sends an email with full error details to the team. How to Customize Nodes ETL Log Extractor Add/remove fields based on your ETL log structure. AI Classification Modify the OpenAI prompt for custom severity levels or deep-dive analysis. Google Sheets Logging Adjust columns for environment, job name or log ID. Jira Fields Customize issue type, labels, priority and assignees. Add-Ons (Extend the Workflow) Send Slack or Teams alerts for high severity issues Store full logs in cloud storage (S3, Supabase, GCS) Add daily/weekly error summary reports Connect monitoring tools like Datadog or Grafana Trigger automated remediation workflows Use Case Examples Logging all ETL failures to Google Sheets Auto-creating Jira tickets with AI-driven severity Summarizing large logs with AI for quick analysis Centralized monitoring of multiple ETL pipelines Reducing manual debugging effort across teams Troubleshooting Guide | Issue | Possible Cause | Solution | |-------|----------------|----------| | Sheets not updating | Wrong Sheet ID or missing permission | Reconnect and reselect the sheet | | Jira ticket fails | Missing required fields or invalid project key | Update Jira mapping | | AI output empty | Invalid OpenAI key or exceeded usage | Check API key or usage limits | | Severity always “low” | Prompt too broad | Adjust AI prompt with stronger rules | | Log preview empty | Incorrect error field mapping | Verify the structure of the ETL error JSON | Need Help? For assistance setting up this workflow, customizing nodes or adding additional features, feel free to contact our n8n developers at WeblineIndia. We can help configure, scale or build similar automation workflows tailored to your ETL and business requirements.

Say Goodbye to Manual Prospect Research! Imagine walking into every discovery call fully prepared with company background, recent news, and perfectly tailored questions, without spending a single minute prepping. This AI-powered workflow connects to your Google Calendar, looks for upcoming discovery calls, sends an AI out to do deep web research on your prospects, and drops a neat briefing right into Slack and Google Drive. By fully automating the research phase, you get over 45 minutes of your life back per call. Why You'll Love This Workflow Get your time back:** Cuts prep time from 45 minutes to zero. Save money:** No need to pay for pricey sales intelligence tools or hire virtual assistants just to google companies. Zero duplicates:** It’s smart enough to remember which calls it’s already prepped for, so you won't get annoyed by duplicate alerts. Built to scale:** Whether you have 1 call tomorrow or 20, the prep quality stays exactly the same. Who Is This For? Sales Reps (AEs & SDRs):** Nail those high-stakes discovery calls with automated background checks and news updates. Founders & Consultants:** Spot pain points and company growth signals before you even say "hello" to position your services better. Agency Ops Teams:** Standardize how your team preps and automatically organize all client data neatly into Google Drive folders. How The Magic Happens The Kickoff: Every day at 4:00 PM, the automation wakes up to prep for tomorrow's meetings. Finding the Meetings: It grabs all your upcoming events from Google Calendar. Filtering: It specifically looks for keywords (like "discovery" or "call") and ignores any meetings it has already researched for you. The AI Researcher: An AI agent uses Firecrawl to scour the web, read the company’s website, and dig up recent news, leadership changes, and funding info. Filing It Away: The workflow creates a dedicated client folder in Google Drive (or finds the existing one) and saves a structured research doc. The Ping: You get a handy summary in your #ops Slack channel with the brief and a direct link to the Drive folder. What You Need to Get Started n8n instance (The engine that runs the automation) Google Calendar (To find your meetings) OpenAI API (The brain doing the research) Firecrawl API (For reading websites) Google Drive (For saving the docs) Slack (For sending you alerts) Easy Setup Steps Import the JSON file to your n8n instance. Connect your accounts: Log into your Google, OpenAI, Firecrawl, and Slack accounts so the workflow has permission to do its job. Tell it where to put things: In the Search Client Folder node, pick your main "CLIENTS" Drive folder. In the Notify Ops Channel node, pick the Slack channel you want the alerts to go to. Tweak the keywords: If you call your meetings something specific (like "Intro" or "Demo"), update the keywords in the Extract Call Details node. Give it a test run: Trigger the workflow manually to make sure it catches a test event and generates a brief! Make It Your Own The Basics: Change the trigger words to match how you name your meetings. Adjust the timer! If you'd rather have the briefs run at 6:00 AM instead of 4:00 PM, just change the schedule node. Take It to the Next Level: LinkedIn Sync:** Add a tool like Proxycurl to pull the prospect's actual LinkedIn profile. CRM Integration:** Have it drop the notes directly into Salesforce or HubSpot instead of just Google Drive. Email Delivery:** Set it up to email the brief to you 15 minutes before the meeting starts. Quick Troubleshooting Not finding any events?** Make sure your calendar invites actually have the trigger words in the title. Research coming up blank?** If the prospect used a personal @gmail.com email to book, the AI won't be able to scan a company website. Getting duplicates?** If you restart or move your n8n instance, it might "forget" what it already researched. Created by: Khairul Muhtadin | Khaisa Studio Category: Sales & CRM | Tags: AI Agent, Google Workspace, Slack, Research, Automation Need custom workflows? Contact us Connect with the creator: Portfolio • Store • LinkedIn • Medium • Threads

Overview Use your voice or text to command a Telegram-based AI agent that scrapes leads or generates detailed research reports—instantly. This workflow turns your Telegram bot into a full-blown outbound machine. Just tell it what type of leads you need, and it’ll use Apollo to find and save them into a spreadsheet. Or drop in a LinkedIn profile, and it’ll generate a personalized research dossier with info like job title, company summary, industry insights, and more. It handles voice messages too—just speak your request and get the results sent back like magic. Who’s it for Cold emailers and growth marketers Solo founders running outbound SDRs doing daily prospecting Agencies building high-quality lead lists or custom research for clients How it works Triggered by a message (text or voice) in Telegram If it’s voice, it transcribes using OpenAI Whisper Uses an AI agent to interpret intent: scrape leads or research a person For lead scraping: Gathers criteria (e.g., location, job title) via Telegram Calls the Apollo API to return fresh leads Saves the leads to Google Sheets For research reports: Takes a LinkedIn profile link Uses AI and lead data tools to create a 1-page professional research report Sends it back to the user via email Example outputs Lead scraping**: Populates a spreadsheet with names, roles, LinkedIn links, company info, emails, and more Research report**: A formatted PDF-style brief with summary of the person, company, and key facts How to set up Connect your Telegram bot to n8n Add your OpenAI credentials (for Whisper + Chat agent) Plug in your Apollo API key or scraping tool Replace the example spreadsheet with your own Customize the prompts for tone or data depth (Optional) Add PDF generation or CRM sync Requirements Telegram Bot Token OpenAI API Key Apollo (or other scraping API) credentials LinkedIn URLs for research functionality How to customize Replace Apollo with Clay, People Data Labs, or another scraping tool Add a CRM push step (e.g. Airtable, HubSpot, Notion) Add scheduling to auto-scrape daily Reformat the research report as a downloadable PDF Change the agent’s tone or role (e.g. “Outreach Assistant,” “Investor Scout,” etc.)

Quick Overview This workflow listens for Aimfox webhooks when a LinkedIn connection request is accepted, notifies a Slack channel, creates and enriches a lead in Airtable using Apify scrapers for profile/company data and website content, and uses OpenAI to generate a lead rating, outreach angles, and hooks. How it works Receives a webhook from Aimfox when a lead accepts a LinkedIn connection request. Extracts the lead, campaign, and LinkedIn profile identifier from the webhook payload and posts a notification to Slack. Creates a new lead record in Airtable with the lead’s name, campaign, date, and LinkedIn profile URL. Uses Apify to scrape the lead’s LinkedIn profile details, the most recent lead post, the lead’s company details, and the most recent company post. Downloads the company website with an HTTP request and converts the HTML content to Markdown for easier analysis. Sends the aggregated lead, company, posts, and website data to OpenAI to produce a structured lead rating, automation outreach angles, and outreach hooks. Updates the Airtable record with the scraped enrichment fields and the OpenAI-generated insights, and sets the lead status to “not contacted”. Setup In Aimfox, configure the “accepted connection request” webhook to point to this workflow’s webhook URL. Add credentials for Slack, Airtable (Personal Access Token), Apify, and OpenAI. Update the Slack channel target and the Airtable base/table to match your workspace, and ensure the table contains fields that match the workflow’s mapped columns (for example Headline, About, Company Website, Hooks, and Icp match).

🧠 AI Chatbot with RAG: Google Gemini & Supabase Vector Store 📌 Summary Build a custom, intelligent knowledge base in minutes. This n8n workflow provides a complete Retrieval-Augmented Generation (RAG) system using Google Gemini and Supabase. It features a seamless dual-flow design: an ingestion pipeline to process and store your uploaded documents, and a conversational AI agent that queries those documents to provide accurate, context-aware answers while remembering past interactions. ✨ Key Features Two-in-One Architecture:** Combines both the document ingestion pipeline and the conversational chat interface into a single, cohesive workflow template. State-of-the-Art AI:** Leverages Google Gemini (models/gemini-embedding-001 and models/gemini-2.5-flash) for high-quality text embeddings and intelligent chat generation. Persistent Conversational Memory:** Uses PostgreSQL to remember chat histories per sessionId, allowing the AI to maintain context across ongoing conversations. The chat trigger automatically generates a unique Session ID per browser window, keeping individual user conversations completely separate. Vector-Powered Accuracy:* Integrates with Supabase (pgvector) to retrieve the top 5 most relevant chunks, ensuring the agent answers based *strictly on your uploaded company documents without hallucinating. Global Error Handling:** Built-in error triggers actively catch API rate limits, parsing failures, and bad requests, formatting them into clear alerts ready to be routed to your team. 🛠️ How It Works Data Ingestion (Knowledge Base Setup): * Documents are uploaded via the Form Trigger and validated for size (<10MB) to prevent parsing timeouts. The text is parsed and split into optimized 1000-character chunks with a 100-character overlap. Gemini generates embeddings for these chunks, which are permanently stored in your Supabase Vector Store. Query Processing (Chat Interface): A user asks a question via the Chat Trigger. The AI Agent accesses the Postgres database to load previous chat context for that specific user's session. The Agent uses the Supabase Retriever tool to pull the top 5 most relevant document chunks based on the user's query. Gemini formulates a concise, factual response citing the retrieved data. 🚀 Setup Instructions 1. Create Database Tables Before running the workflow, ensure your Supabase/Postgres database has the required tables. ⚠️ See the "DATABASE SETUP (SQL)" sticky note on the canvas for the complete SQL script. 2. Connect Credentials Connect your Google Gemini API, Supabase (URL and Service Role Key), and Postgres databases. (Note: Your Postgres memory can be hosted within your Supabase project). Ensure your Supabase database has pgvector enabled. 3. Update Table Names Open the Supabase and Postgres nodes and replace: your_documents_table → your actual table name your_chat_history_table → your actual table name 4. Configure Error Notifications (Important) The workflow catches errors and formats an alert message, but you must manually connect a messaging node (like Slack, Microsoft Teams, or Email) directly after the Format Error Alert node to receive these notifications. 5. Ingest Data Open the Upload Knowledge Base Form node, click "Test step", and upload a sample document to index it into your database. 6. Test the Agent Open the User Chat Trigger node, click "Chat", and ask a question related to the document you just uploaded! 📦 Nodes Used User-Facing Interfaces: Chat Trigger** - The user-facing chat interface for asking questions (includes Pin Data for easy testing). Form Trigger & File Validator** - Provides a simple UI to upload and size-check files, with explicitly stated rules for the end-user. Includes a Webhook Response for success confirmation. Data Processing Pipeline (Ingestion): Document Default Data Loader** - Extracts raw text from uploaded files. Character Text Splitter** - Chunks large texts into digestible pieces (1000 chars) for the AI. Embeddings Google Gemini** - Converts text chunks into mathematical vectors. Supabase Inserter** - Stores vectors in the knowledge base. AI & Memory (Query): Supabase Retriever** - Performs semantic search to find top 5 relevant chunks. Postgres Chat Memory** - Stores and retrieves historical chat context by session ID. Google Gemini Chat Model** - The core LLM powering the conversational responses. Knowledge-Base AI Agent** - Orchestrates the memory, tools, and LLM to answer the user's prompt. Error Handling: Error Trigger & Format Error Alert** - Catches global execution failures. Connect an output to route these formatted alerts to your team. ⚠️ Limitations & Guidelines File Size & Type:** The workflow limits files to 10MB to prevent memory exhaustion, and currently supports .pdf, .txt, .docx, .pptx. Highly complex PDFs with nested tables or un-OCR'd images may fail parsing. Context Window Limits:** While the Agent tracks sessions, very long ongoing conversations (e.g., hundreds of messages) will eventually hit the LLM's token limit. It is recommended to implement a cron job or separate workflow to prune the chat_history table periodically or clear old sessions. API Costs:** Each document ingestion and chat query uses Gemini API calls (embeddings + chat). Monitor your API usage.

Quick Overview This workflow runs daily (or manually) to pull the last 24 hours of AI and blockchain headlines from Google News via SerpAPI, uses Groq Llama 3.3 to draft Twitter/X and LinkedIn posts, generates a matching image with Pollinations, then saves everything to Google Sheets and notifies Slack. How it works Runs on a daily 8 AM schedule (or via a manual trigger for testing). Queries Google News through SerpAPI for the last day’s AI, artificial intelligence, blockchain, and crypto headlines. Builds a numbered news digest and stops with an error if no results are returned. Sends the digest to Groq (Llama 3.3 70B) to generate a Twitter/X post and a LinkedIn post in JSON format. Generates a 1024×1024 social image with Pollinations based on the Twitter/X post, then uploads it to Google Drive and makes it publicly accessible. Appends the generated posts and public image URL to Google Sheets, logs the successful run to a separate sheet, and posts a success notification to a Slack channel. Setup Add a SerpAPI credential using HTTP Query Auth and provide your API key as the api_key query parameter. Add a Groq API credential and ensure the Groq Llama 3.3 chat model is selected. Add a Pollinations Bearer token credential (or adjust the request to use the anonymous tier if applicable). Connect Google Drive OAuth and choose the destination folder (or leave it as My Drive) for image uploads. Connect Google Sheets OAuth and select the target spreadsheet and sheet tabs for both the content append and the run log append steps. Connect Slack OAuth and set the channel where the workflow posts the success notification.

Quick Overview This workflow listens for a Telegram message containing an idea or URL, extracts any linked page text, uses OpenAI to generate LinkedIn/X/Instagram captions plus an image prompt, creates an illustration via AtlasCloud (Grok Imagine), then publishes the posts with the image to LinkedIn, X, and Instagram using Blotato. How it works Receives a Telegram message with an idea, topic, or URL. If the message includes a link, fetches the web page and extracts a cleaned text snippet to use as reference context. Uses OpenAI (via an AI agent with conversation memory) to generate a LinkedIn post, an X caption, an Instagram caption, and a detailed image-generation prompt in the specified brand voice. Sends the image prompt to AtlasCloud’s image generation API (Grok Imagine) and polls the prediction endpoint until the image is completed or errors. Extracts the generated image URL and publishes the image with the platform-specific captions to LinkedIn, X, and Instagram via Blotato. Sends a confirmation message back to Telegram including the generated image URL. Setup Create and connect Telegram bot credentials, then message the bot using the Telegram Trigger webhook. Add an OpenAI API credential and ensure the selected chat model is available in your OpenAI account. Add an AtlasCloud API key as HTTP Header Auth and confirm the configured model name, aspect ratio, and resolution match your AtlasCloud account capabilities. Install and configure the Blotato community node, connect your Blotato API credential, and replace the LinkedIn/X/Instagram account IDs in each publish step with your own.

Trend2Content This n8n workflow (named Trend2Content) takes a short topic input from a small web form, scrapes recent/top social content for that topic (via an Apify act), aggregates the raw text, passes that aggregated content into a LangChain AI agent (Google Gemini in this flow) which returns a structured content output (topic summary, blog post title ideas, tweet hooks), formats that output, and appends the results into a Google Sheet. It’s a lightweight: Topic → Trending Content → AI Ideas → Sheet pipeline for fast content ideation. How It Works (Step-by-Step) On Form Submission The user fills a single field Topic (webhook/form trigger). X Scraper (HTTP Request) Calls an Apify act run-sync-get-dataset-items with: searchTerms: [{{ $json.Topic }}] maxItems: 20 to fetch social posts for that topic. Edit Fields (Set) Extracts fullText from each scraped item and stores it in a Content field. Aggregate Aggregates the Content field so the AI agent receives one combined input rather than many separate items. Google Gemini Chat Model (LM) + AI Agent (LangChain Agent Node) The agent uses a templated system prompt + the aggregated content to generate a structured response with: Topic summary Blog title ideas Tweet hooks The agent is connected to a Structured Output Parser node to force a predictable JSON schema. Code in JavaScript Transforms the structured JSON into sheet-friendly strings (joins arrays with bullets). Append Row in Sheet (Google Sheets) Appends the generated blog_post_titles and tweet_hooks to the target Google Sheet. (Optional) Sticky notes and internal meta nodes exist for documentation and board organization. Quick Setup Guide 👉 Demo & Setup Video 👉 Sheet Template 👉 Course Nodes of Interest You Can Edit 1. On Form Submission (formTrigger) Edit form fields (add author, language, region, or filters). Change webhook behaviour or require authentication. 2. X Scraper (HTTP Request) URL:** Change to another Apify act or another scraping API. jsonBody:** Change maxItems, sort (Top/Recent), or modify searchTerms (e.g., topic + hashtag). Headers:** Set the Authorization: Bearer token (Apify). Add pagination or query parameters if switching scraper APIs. 3. Edit Fields (Set) Map additional fields (author, date, source URL). Add filtering logic (remove short posts, retweets, duplicates). 4. Aggregate Customize aggregation strategy: Concatenate Sample top N Deduplicate before combining 5. Google Gemini Chat Model / AI Agent / Structured Output Parser Edit systemMessage and prompt template (tone, format, extra outputs). Tune LM parameters (temperature, max tokens). Update schema to request: Sentiment Key quotes Additional formats 6. Code in JavaScript Modify formatting (CSV-ready, add timestamp). Add metadata columns. Add deduplication or length checks before write. 7. Append Row in Sheet (Google Sheets) Change spreadsheet ID or sheet name. Add more columns. Switch from Append to Upsert. Configure batch appends. What You’ll Need (Credentials) 1. Apify API Token Used in the HTTP Request node. Set in header: Authorization: Bearer YOUR_APIFY_TOKEN 2. Google Sheets OAuth2 Credentials Must include spreadsheets scope. Required for appending rows. 3. Google / PaLM / Google Gemini API Credentials Used by the LangChain / Google Gemini node. Optional n8n webhook URL (for mounting the form). Monitoring credentials (Slack webhook, Sentry, etc.) for alerts. Recommended Settings & Best Practices Enable workflow only after testing (active: true). Limit maxItems (20–50 recommended). Sanitize & dedupe content before sending to the AI. Always use a Structured Output Parser for reliable JSON. Set low temperature (0.0–0.6) for consistent results. Add retries and exponential backoff for external APIs. Add logging or Slack alerts for failures. Keep execution log columns in the sheet (status, error_message, run_time). Store workflow JSON in version control. Monitor API rate limits (Apify + Google). Avoid writing scraped PII into public sheets. Customization Ideas Add output types: Instagram captions LinkedIn posts Video scripts Email subject lines Add sentiment / trend scoring. Add language detection & translation. Store aggregated content in a vector database (Pinecone / Chroma). Schedule runs using Cron trigger. Add multiple data sources (Reddit, RSS, HackerNews). Add approval workflow (Slack / Notion). Add metadata columns: source_urls top_authors most_shared Tags #content-ideation' #social #ai #google-gemini #apify #google-sheets #n8n