by Mychel Garzon
Quick overview Three specialized AI agents: Researcher, Writer, and Reviewer, collaborate autonomously to research a topic, draft content, and quality-check it through a self-correcting review loop with a circuit breaker. Approved content is published directly to Notion with full audit metadata. How it works A form submission captures the research topic, content format (blog post, exec summary, or LinkedIn post), target audience, and maximum revision cycles allowed before the circuit breaker fires. The Researcher Agent uses GPT-4o and Tavily Search to gather authoritative sources, extract key facts and contradictions, and produce a validated research brief. Search depth and result count are automatically scaled to the requested format. The Writer Agent receives the research brief and produces a structured draft in the correct format. On revision cycles it also receives the Reviewer's specific rejection notes and must address them directly. A Circuit Breaker sits between the Writer and Reviewer. If the number of Reviewer rejections reaches the user-defined maximum, the draft is force-approved and the pipeline proceeds to publishing without another LLM call. The Reviewer Agent evaluates the draft against the original sources on three axes — accuracy, clarity, and completeness — each scored 0–10. All three must score 7 or above for approval. On rejection, detailed revision notes are injected back into the Writer's next prompt. Once approved (or circuit-broken), the pipeline builds a full Notion block structure including a metadata callout with scores and revision stats, then creates the Notion page and appends content blocks in rate-limited batches of 25. After the batch loop completes, a post-loop aggregation step checks for any failed batches and appends a warning callout to the Notion page if needed. A separate Error Trigger path logs any pipeline failures to a Notion monitoring database. Setup Open the Initialize State Variables code node and replace the two placeholder constants at the top: NOTION_CONTENT_DB_ID with your content database ID and NOTION_MONITORING_DB_ID with your monitoring database ID. Add your OpenAI API credential to all three chat model nodes: OpenAI GPT-4 Agent 1 (Researcher), OpenAI GPT-4 Agent 2 (Writer), and OpenAI GPT-4 Agent 3 (Reviewer). Add your Tavily API credential to the Perform Tavily Search tool node inside the Researcher Agent. Add your Notion API credential to the Generate Notion Page and Log to Notion on Error nodes. The two HTTP Request nodes that append blocks (Append Blocks to Notion, Add Warning Notification) use the same Notion credential type and will prompt you on first run. In your Notion workspace, share both databases (content and monitoring) with your Notion integration. The content database needs at minimum a Title property. The monitoring database needs Title, and optionally text properties for Failed Node, Error Message, and Execution ID. Requirements OpenAI API key (GPT-4o) Tavily API key Notion API integration with access to a content database and a monitoring database Customization Swap GPT-4o for any other n8n-compatible chat model by replacing the three OpenAI Chat Model nodes Adjust the Reviewer's approval threshold (currently 7/10 on all three axes) inside the Reviewer Agent prompt Change the circuit breaker limit at form submission — no code changes needed, it's a form field Modify batch size (default 25 blocks) or rate-limit wait (default 350ms) in Prepare Notion Block Batches and Wait 350ms nodes Edit agent system prompts and format requirements to support additional content types beyond blog post, exec summary, and LinkedIn post
by Hybroht
Source Discovery - Automatically Search More Up-to-Date Information Sources 🎬 Overview Version : 1.0 This workflow utilizes various nodes to discover and analyze potential sources of information from platforms like Google, Reddit, GitHub, Bluesky, and others. It is designed to streamline the process of finding relevant sources based on specified search themes. ✨ Features Automated source discovery from multiple platforms. Filtering of existing and undesired sources. Error handling for API requests. User-friendly configuration options. 👤 Who is this for? This workflow is ideal for researchers, content marketers, journalists, and anyone looking to efficiently gather and analyze information from various online sources. 💡 What problem does this solve? This workflow addresses the challenge of manually searching for relevant information sources, saving time and effort while ensuring that users have access to the most pertinent content. Ideal use-cases include: Resource Compilation for Academic and Educational Purposes Journalism and Research Content Marketing Competitor Analysis 🔍 What this workflow does The workflow gathers data from selected platforms through search terms. It filters out known and undesired sources, analyzes the content, and provides insights into potential sources relevant to the user's needs. 🔄 Workflow Steps 1. Search Queries Fetch sources using SerpAPI search, DuckDuckGo, and Bluesky. Utilizes GitHub repositories to find relevant links. Leverages RSS feeds from subreddits to identify potential sources. 2. Filtering Step Removes existing and undesired sources from the results. 3. Source Selection Analyzes the content of the identified sources for relevance. 📌 Expected Input / Configuration The workflow is primarily configured via the Configure Workflow Args (Manual) node or the Global Variables custom node. Search themes: Keywords or phrases relevant to the desired content. Lists of known sources and undesired sources for filtering. 📦 Expected Output A curated list of potential sources relevant to the specified search themes, along with insights into their content. 📌 Example ⚙️ n8n Setup Used n8n version:** 1.105.3 n8n-nodes-serpapi:** 0.1.6 n8n-nodes-globals:** 1.1.0 n8n-nodes-bluesky-enhanced**: 1.6.0 n8n-nodes-duckduckgo-search**: 30.0.4 LLM Model:** mistral-small-latest (API) Platform:** Podman 4.3.1 on Linux Date:** 2025-08-06 ⚡ Requirements to Use / Setup Self-hosted or cloud n8n instance. Install the following custom nodes: SerpAPI, Bluesky, and DuckDuckGo Search. n8n-nodes-serpapi n8n-nodes-duckduckgo-search n8n-nodes-bluesky-enhanced Install the Global Variables Node for enhanced configuration: n8n-nodes-globals (or use Edit Field (Set) node instead) Provide valid credentials to nodes for your preferred LLM model, SerpAPI, and Bluesky. Credentials for GitHub recommended. ⚠️ Notes, Assumptions \& Warnings Ensure compliance with the terms of service of any platforms accessed or discovered in this workflow, particularly concerning data usage and attribution. Monitor API usage to avoid hitting rate limits. The workflow may encounter errors such as 403 responses; in such cases, it will continue by ignoring the affected substep. Duplicate removal is applied, but occasional overlaps might still appear depending on the sources. This workflow assumes familiarity with n8n, APIs, and search engines. Using AI agents (Mistral or substitute LLMs) requires access to their API services and keys. This is not a Curator of News. It is designed to find websites that are relevant and useful to your searches. If you are looking for a relevant news selector, please check this workflow. ℹ️ About Us This workflow was developed by the Hybroht team. Our goal is to create tools that harness the possibilities of technology and more. We aim to continuously improve and expand functionalities based on community feedback and evolving use cases. For questions, reach out via contact@hybroht.com. ⚖️ Warranty & Legal Notice This free workflow is provided "as-is" without any warranties of any kind, either express or implied, including but not limited to the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. By using this workflow, you acknowledge that you do so at your own risk. We shall not be held responsible for any damages, losses, or liabilities arising from the use or inability to use this workflow, including but not limited to any direct, indirect, incidental, or consequential damages. It is your responsibility to ensure that your use of this workflow complies with all applicable laws and regulations.
by Daniel Shashko
Quick overview This workflow runs weekly to fetch recently lost backlinks from DataForSEO, deduplicate them by referring domain, and triage each loss with a live status check. It logs results to Google Sheets, drafts recovery email angles with OpenAI for actionable removals, and posts a digest to Slack. How it works Runs every Monday morning (or manually) and loads your domain and thresholds such as how many days back to check and spam/rank limits. Queries the DataForSEO Backlinks API for backlinks with status “lost” seen within the configured time window. Deduplicates results to one best lost link per referring domain, filters by rank and spam score, and removes links already recorded in the n8n Data Table watch list. For each remaining lost link, checks your target URL live with an HTTP request and classifies the loss into recovery buckets (your page gone, your redirect, link removed, their page gone, or unchecked). Uses OpenAI to write one-sentence outreach angles only for links classified as “removed while both pages are healthy.” Builds a Slack digest summarizing counts and top items, appends the full triage rows to a Google Sheets tab, and upserts each processed link into the n8n Data Table so it isn’t re-reported. If there is nothing new (or the API response indicates a failure), posts a “nothing new / API issue” message to Slack, and if the workflow errors, posts an alert message to a separate Slack channel. Setup Add a DataForSEO HTTP Basic Auth credential (login and password) and ensure your account can access the Backlinks API. Update the Settings values (domain, lost_since_days, min_rank, max_spam_score, caps, and Slack channel names) to match your environment. Connect Google Sheets credentials and replace the spreadsheet URL/ID and target sheet name where the “Lost links” rows are appended. Connect Slack credentials and set the digest channel plus the separate #alerts (or your preferred) channel for error notifications. Create an n8n Data Table named lost_link_watch with text columns link_id, bucket, and seen_on to track processed losses. Add an OpenAI credential and ensure the selected chat model is available in your account (or change it to a model you can use).
by Trung Tran
Cloudflare Incident Monitoring & Escalation Workflow 🚀 Try Decodo — Web Scraping & Data API (Coupon: TRUNG) Decodo is a powerful public data access platform offering managed web scraping APIs and proxy infrastructure to collect structured web data at scale. It handles proxies, anti-bot protection, JavaScript rendering, retries, and global IP rotation—so you can focus on data, not scraping complexity. Why Decodo Managed Web Scraping API with anti-bot bypass & high success rates Works with JS-heavy sites; outputs JSON/HTML/CSV Easy integration (Python, Node.js, cURL) for eCommerce, SERP, social & general web data 🎟️ Special Discount Use coupon TRUNG to get the Advanced Scraping API plan — 23,000 requests for $5. Who this workflow is for For DevOps, SRE, IT Ops, and Platform teams running production traffic behind Cloudflare who need reliable incident awareness without alert fatigue. Use it if you want: Continuous Cloudflare incident monitoring Clear severity-based routing Automatic escalation into JIRA Clean Slack & Telegram notifications Deduplicated, noise-controlled alerts What this workflow does This workflow polls the Cloudflare Status API, detects unresolved incidents, scores their impact, and routes them to the right channels. High-impact incidents are escalated to JIRA. Lower-impact updates are notified (or skipped) to reduce noise. How it works (high level) Runs on a fixed schedule (e.g. every 5 minutes) Fetches current Cloudflare incidents Stops early if no active issues exist Normalizes and scores incidents (severity, impact, affected service) Deduplicates previously-alerted incidents Builds human-readable notification payloads Routes by impact: High → create JIRA incident + notify Low → notify or suppress Sends alerts to Slack and Telegram Requirements Decoco Scrapper API credential n8n (self-hosted or Cloud) Cloudflare Status API (public) Slack bot (chat:write) Telegram bot + chat ID JIRA project with issue-create permission Optional LLM credentials (summarization/classification) Notes All secrets are stored in n8n Credentials Workflow is idempotent and safe to rerun No assumptions about root cause or remediation Built for production-grade incident visibility with n8n.
by Cheng Siong Chin
How It Works This workflow automates environmental, social, and governance (ESG) data collection, compliance validation, and sustainability reporting for corporations managing complex regulatory requirements and stakeholder transparency expectations. Designed for sustainability officers, compliance teams, and investor relations departments, it solves the challenge of aggregating ESG metrics across global operations, validating data accuracy, and generating standardized reports for multiple frameworks. The system schedules regular monitoring, fetches consolidated ESG data from operational systems, generates S&D (sustainability and disclosure) submissions, validates compliance through dual AI agents (Compliance Analyzer ensures regulatory adherence, Decision Coordination orchestrates specialized sub-agents for aggregate analysis, traceability monitoring, summary generation, and governance reporting), checks star ratings for data quality, routes findings by compliance status (critical/routine), and produces standardized reports with traceability records. Organizations achieve 90% reduction in reporting cycle time, ensure multi-framework compliance, eliminate manual data aggregation errors, and maintain complete audit trails for regulatory scrutiny. Setup Steps Connect Schedule Trigger for monitoring frequency Configure ESG data sources with API credentials Add AI model API keys to Compliance Analyzer and Decision Coordination Agent nodes Define reporting frameworks and compliance requirements in agent prompts Set quality rating thresholds for data completeness and materiality scoring parameters Configure alert mechanisms for critical compliance gaps requiring immediate remediation Prerequisites ESG data management system access, AI service accounts Use Cases Carbon emissions tracking and reporting, supply chain sustainability monitoring Customization Modify agent prompts for industry-specific materiality topics Benefits Reduces reporting cycle time by 90%, ensures multi-framework compliance simultaneously
by Airtop
Extract Facebook Group Posts with Airtop Use Case Extracting content from Facebook Groups allows community managers, marketers, and researchers to gather insights, monitor discussions, and collect engagement metrics efficiently. This automation streamlines the process of retrieving non-sponsored post data from group feeds. What This Automation Does This automation extracts key post details from a Facebook Group feed using the following input parameters: Facebook Group URL**: The URL of the Facebook Group feed you want to scrape. Airtop Profile**: The name of your Airtop Profile authenticated to Facebook. It returns up to 5 non-sponsored posts with the following attributes for each: Post text Post URL Page/profile URL Timestamp Number of likes Number of shares Number of comments Page or profile details Post thumbnail How It Works Form Trigger: Collects the Facebook Group URL and Airtop Profile via a form. Browser Automation: Initiates a new browser session using Airtop. Navigates to the provided Facebook Group feed. Uses an AI prompt to extract post data, including interaction metrics and profile information. Structured Output: The results are returned in a defined JSON schema, ready for downstream use. Setup Requirements Airtop API Key — Free to generate. An Airtop Profile logged into Facebook. Next Steps Integrate With Analytics Tools**: Feed the output into dashboards or analytics platforms to monitor community engagement. Automate Alerts**: Trigger notifications for posts matching certain criteria (e.g., high engagement, keywords). Combine With Comment Automation**: Extend this to reply to posts or engage with users using other Airtop automations. Let me know if you’d like this saved as a .md file or included in your Airtop automation library. Read more about how to extract posts from Facebook groups
by Vadym Nahornyi
> ⚠️ Multi-language WhatsApp Error Notifier Get instant WhatsApp alerts when any workflow fails — perfect for mobile-first monitoring and fast incident response. ✅ No coding required ✅ Works with any workflow via Error Workflow ✅ Step-by-step setup instructions included in: 🇬🇧 English 🇪🇸 Español 🇩🇪 Deutsch 🇫🇷 Français 🇷🇺 Русский 📦 What This Template Does This template sends real-time WhatsApp notifications when a workflow fails. It uses the WhatsApp Business Cloud API to deliver a preformatted error message directly to your phone. The message includes: Workflow name Error message Last executed node Example message: Error on WorkFlow: {{ $json.workflow.name }} Message: {{ $json.execution.error.message }} lastNodeExecuted: {{ $json.execution.lastNodeExecuted }} ⚙️ Prerequisites Before using this template, make sure you have: A verified Facebook Business account Access to WhatsApp Business Cloud API A sender phone number (registered in Meta) An access token (used as credentials in n8n) A pre-approved message template (or be within the 24h session window) More info from Meta Docs → 🚀 How to Use Open the template and insert your WhatsApp credentials Enter your target phone number (e.g. your own) in international format Customize the message body if needed Save the workflow but do not activate it In any other workflow → open Settings → set this as your Error Workflow 🌐 Multi-language Setup Guide Included This template includes full setup instructions with screenshots and message formatting help in: 🇬🇧 English 🇪🇸 Español 🇩🇪 Deutsch 🇫🇷 Français 🇷🇺 Русский Choose your language inside the embedded sticky note in the workflow.
by Mark Shcherbakov
Video Guide I prepared a detailed guide that showed the whole process of integrating the Binance API and storing data in Airtable to manage funding statements associated with tokens in a wallet. Youtube Link Who is this for? This workflow is ideal for developers, financial analysts, and cryptocurrency enthusiasts who want to automate the process of managing funding statements and token prices. It’s particularly useful for those who need a systematic approach to track and report funding fees associated with tokens in their wallets. What problem does this workflow solve? Managing funding statements and token prices across multiple platforms can be cumbersome and error-prone. This workflow automates the process, allowing users to seamlessly fetch funding fees from Binance and record them alongside token prices in Airtable, minimizing manual data entry and potential discrepancies. What this workflow does This workflow integrates the Binance API with an Airtable database, facilitating the storage and management of funding statements linked to tokens in a wallet. The agent can: Fetch funding fees and current positions from Binance. Aggregate data to create structured funding statements. Insert records into Airtable, ensuring proper linkage between funding data and tokens. API Authentication: The workflow establishes authentication with the Binance API using a Crypto Node to handle API keys and signatures, ensuring secure and verified requests. Data Collection: It retrieves necessary data, including funding fees and current positions with properly formatted API requests to ensure seamless communication with Binance. Airtable Integration: The workflow inserts aggregated funding statements and token data into the corresponding Airtable records, managing token existence checks to avoid duplicate entries. Setup Set Up Airtable Database: Create an Airtable base with tables for Funding Statements and Tokens. Generate Binance API Key: Log in and create an API key with appropriate permissions. Set Up Authentication in N8N: Utilize a Crypto Node for Binance API authentication. Configure API Request to Binance: Set request method and headers for communication with the Binance API. Fetch Funding Fees and Current Positions: Retrieve funding data and current positions efficiently. Aggregate and Create Statements: Aggregate data to create detailed funding statements. Insert Data into Airtable: Input the structured data into Airtable and manage token records. Using Get Price Node: Implement a Get Price Node to maintain current token price tracking without additional setup.
by Sona Labs
Generate Sora videos, stitch clips, and post to Twitter Generate creative ASMR cutting video concepts with GPT-5.1, create high-quality video clips using Sora v2, stitch them together with Cloudinary, and automatically post to Twitter/X—transforming ideas into viral content without manual video editing. How it works Step 1: Generate Video Concepts Schedule Trigger activates the workflow automatically GPT-5.1 AI agent generates 3 unique ASMR cutting scene prompts with unusual objects Creates structured video prompts optimized for Sora v2 (frontal camera angle, cutting actions) Generates Twitter-ready captions with relevant hashtags Saves all concepts and scripts to Google Sheets for tracking Step 2: Create Video Clips with Sora v2 Generates 3 separate Sora v2 video clips in parallel (8-12 seconds each) Each clip uses unique prompts from GPT-5.1 output Videos render at 720x1280 resolution (vertical format for social media) System waits 30 seconds for rendering to complete Step 3: Monitor & Download Videos Loops through all 3 video generation requests Checks Sora API status every 30 seconds until rendering completes Automatically skips failed renders (continues workflow with successful videos) Downloads completed videos from Sora API Uploads each clip to Cloudinary for storage and processing Step 4: Stitch Videos Together Collects all uploaded Cloudinary video IDs Builds Cloudinary transformation URL to stitch 3 clips into one seamless video Applies Twitter-compatible encoding (H.264 baseline, AAC audio, MP4 format) Downloads the final stitched video Step 5: Upload to Twitter/X Prepares video file data and calculates total file size Uses Twitter's chunked upload API (INIT → APPEND → FINALIZE) Waits for Twitter's video processing to complete Checks processing status until video is ready Posts tweet with AI-generated caption and attached video Updates Google Sheets status to "Posted" What you'll get AI-Generated Concepts**: Creative ASMR cutting ideas with unusual objects (glass avocados, lava rocks, rainbow soap) Professional Video Clips**: Three 8-12 second Sora v2 videos per concept with 720x1280 resolution Seamless Stitching**: Single combined video optimized for Twitter/X specifications Engaging Captions**: GPT-5.1 generated tweets with hashtags designed for virality Automated Posting**: Direct upload to Twitter/X without manual intervention Cloud Backup**: All videos stored in Cloudinary with metadata Progress Tracking**: Google Sheets integration shows workflow status (In Progress → Posted) Error Handling**: Failed Sora renders are automatically skipped Why use this Save 4+ hours per video**: Eliminate scripting, shooting, editing, and posting time Consistent posting schedule**: Set it and forget it with the Schedule Trigger Scale content creation**: Generate multiple video variations in 20-30 minutes Professional quality**: Leverage Sora v2's AI video generation for realistic cutting scenes Optimize for virality**: GPT-5.1 creates concepts and captions designed for engagement Reduce creative burnout**: AI handles ideation, execution, and distribution No video editing skills needed**: Complete automation from concept to post Test multiple concepts**: Generate 3 variations per run to see what resonates Setup instructions Required accounts and credentials: OpenAI API Key (GPT-5.1 and Sora v2 access required) Sign up at https://platform.openai.com Ensure your account has Sora v2 API access enabled Generate API key from API Keys section Note: Sora v2 is currently in limited beta Google Sheets OAuth (for tracking video ideas and status) Free Google account required Create a spreadsheet with columns: Category, Scene 1, Scene 2, Scene 3, Status n8n will request OAuth permissions during setup Cloudinary Account (for video storage and stitching) Sign up at https://cloudinary.com (free tier available) Note your cloud name from the dashboard Create an upload preset named n8n_integration Enable unsigned uploads for the preset Twitter OAuth 1.0a Credentials (for automated posting) Apply for Twitter Developer access at https://developer.twitter.com Create a new app in the Developer Portal Generate: API Key, API Secret, Access Token, Access Token Secret Enable "Read and Write" permissions (not just Read) OAuth 1.0a is required for media uploads (OAuth 2.0 won't work) Configuration steps: Update OpenAI API Key: Add your OpenAI API key to these nodes: "OpenAI Chat Model" credentials "Create Sora Video Scene - 1" (Authorization header) "Create Sora Video Scene - 2" (Authorization header) "Create Sora Video Scene - 3" (Authorization header) "Check Video Status" (Authorization header) "Download Completed Video" (Authorization header) Replace Bearer API KEY with Bearer YOUR_ACTUAL_API_KEY Configure Google Sheets: Open "Save Category and Clip Scripts" and "Update Status" nodes Authenticate with your Google account (OAuth 2.0) Select your spreadsheet and sheet name Ensure columns match: Category, Scene 1, Scene 2, Scene 3, Status The workflow will update Status from "In Progress" to "Posted" Update Cloudinary Settings: In "Upload to Cloudinary" node: Replace {Cloud name here} in the URL with your Cloudinary cloud name Verify upload preset is set to n8n_integration In "Build Stitch URL" node: Open the Code node Replace dph9n4uei on line 1 with your cloud name This builds the video stitching transformation URL Add Twitter OAuth 1.0a Credentials: Configure OAuth 1.0a in these nodes: "Twitter Upload - INIT" "Twitter Upload - APPEND" "Finalize Upload" "Check Twitter Processing Status" "Post a Tweet" Use the same OAuth 1.0a credential for all nodes Ensure your Twitter app has "Read and Write" permissions Adjust Schedule Trigger (optional): Default: Runs on every interval Modify in "Schedule Trigger" node to set specific times Recommended: Once per day or every few hours to avoid rate limits Test the workflow: Click "Execute Workflow" to test manually first Verify GPT-5.1 generates 3 video concepts Check that Sora v2 creates all 3 videos Confirm Cloudinary stitches videos correctly Ensure Twitter post appears with video and caption Important notes: Sora API Rate Limits**: Sora v2 may have rendering quotas. Monitor your usage Video Rendering Time**: Each Sora clip takes 2-5 minutes. Total workflow: 15-25 minutes Failed Videos**: The workflow automatically skips failed renders and continues Twitter Video Limits**: Maximum 512MB per video, MP4 format required Cloudinary Free Tier**: 25 credits/month includes video transformations Cost Estimate**: ~$1-3 per run (Sora API pricing varies) Troubleshooting: "Sora API access required"**: Contact OpenAI to enable Sora v2 API on your account Twitter upload fails**: Verify OAuth 1.0a credentials have "Read and Write" permissions Cloudinary upload fails**: Check cloud name and ensure upload preset exists Videos don't stitch**: Verify all 3 videos uploaded successfully to Cloudinary Google Sheets not updating**: Confirm OAuth permissions and sheet column names match Next steps: Enable the Schedule Trigger to automate daily/weekly posts Monitor Google Sheets to track posted content Adjust GPT-5.1 prompts in "ASMR Cutting Ideas" for different content themes Experiment with different video durations (8 vs 12 seconds) Add error notifications using Email or Slack nodes
by achiya
Find and share AliExpress affiliate products through Telegram Build a Telegram bot that helps users find AliExpress products using natural language requests. The bot uses OpenAI to optimize search queries, Decodo to scrape product listings, and AI analysis to select the best options based on ratings, reviews, and price—then automatically generates affiliate tracking links for each recommendation. What it does When users send "Find me wireless keyboard": Bot checks user is member of your Telegram channel (optional) Validates command starts with accepted phrases OpenAI generates optimized English search query Decodo scrapes products from AliExpress AI analyzes the top 10 products and selects best 2 based on reviews, ratings, and price AliExpress Affiliate API creates tracking links Bot sends formatted recommendations with images, prices, ratings, and links Who this is for Affiliate marketers monetizing Telegram channels E-commerce entrepreneurs automating recommendations Channel owners adding value while earning commissions Anyone building AliExpress affiliate systems Setup requirements Credentials needed Telegram Bot API Create bot via @BotFather Add token to n8n Make bot admin in your channel AliExpress Affiliate API Sign up for affiliate program Get: App Key, App Secret, Tracking ID Add to n8n OpenAI API Get API key Add to n8n Used for search and analysis Configuration required Before activation: Channel username - Replace @YOUR_CHANNEL in 2 nodes: Check Channel Membership Verify Channel Member Tracking ID - Set YOUR_AFFILIATE_TRACKING_ID in: Generate Affiliate Links Create Affiliate Link Channel URL - Update button in Request Channel Join Bot admin - Make bot admin in your channel How to use User commands Users send messages starting with: Find me [product] Search for [product] Look for [product] Get me [product] Send me [product] Show me [product] Examples: Find me wireless mouse Search for phone case Look for bluetooth speaker Bot responses Non-member: Asks to join channel Invalid format: Shows usage examples Valid request: Sends "searching..." status Processes with AI Returns 2 recommendations Each includes: image, title, price, rating, orders, link "More Results" button available Customization options Product count: Edit "Select Top 2 Products" node Selection criteria: Modify AI prompts in "AI Product Search" Commands: Add/remove in "Validate Command Format" Channel gate: Delete verification nodes to remove Language: Translate Telegram message nodes AI model: Switch to GPT-3.5-turbo for lower costs Technical details Workflow components: Entry: Telegram webhook Verification: Channel membership Validation: Command format Processing: AI query → Decodo scrape → AI analysis Output: Affiliate links → Message format → Send APIs used: Telegram Bot API - User interaction OpenAI API - Search optimization, product analysis Decodo - AliExpress scraping AliExpress Affiliate API - Link generation Error handling: Invalid commands → Usage guide Non-members → Join request No results → Error message Spam → Auto-removal Best practices Cost management: OpenAI: $0.01-0.05 per search Cache popular searches Use GPT-3.5 for lower costs Security: Store credentials in n8n Rotate API keys regularly Monitor activity Performance: Use webhook mode Set up error notifications Implement rate limiting Troubleshooting Bot not responding Verify workflow activated Check credentials valid Review error logs Channel verification fails Confirm bot is admin Check @username correct Ensure user joined No products found Validate credentials Check tracking ID Try different terms Links broken Confirm account active Verify tracking ID Check permissions Version Version: 1.0 Updated: January 2026 Compatible: n8n v1.0+ Setup: 10-15 minutes
by Mark Shcherbakov
Video Guide I prepared a detailed guide that shows the whole process of building an AI tool to analyze Instagram Reels using n8n. Youtube Link Who is this for? This workflow is ideal for social media analysts, digital marketers, and content creators who want to leverage data-driven insights from their Instagram Reels. It's particularly useful for those looking to automate the analysis of video performance to inform strategy and content creation. What problem does this workflow solve? Analyzing video performance on Instagram can be tedious and time-consuming, requiring multiple steps and data extraction. This workflow automates the process of fetching, analyzing, and recording insights from Instagram Reels, making it simpler for users to track engagement metrics without manual intervention. What this workflow does This workflow integrates several services to analyze Instagram Reels, allowing users to: Automatically fetch recent Reels from specified creators. Analyze the most-watched videos for insights. Store and manage data in Airtable for easy access and reporting. Initial Trigger: The process begins with a manual trigger that can later be modified for scheduled automation. Data Retrieval: It connects to Airtable to fetch a list of creators and their respective Instagram Reels. Video Analysis: It handles the fetching, downloading, and uploading of videos for analysis using an external service, simplifying performance tracking through a structured query process. Record Management: It saves relevant metrics and insights into Airtable, ensuring that users can access and organize their video analytics effectively. Setup Create accounts: Set up Airtable, Edify, n8n, and Gemini accounts. Prepare triggers and modules: Replace credentials in each node accordingly. Configure data flow: Ensure modules are set to fetch and analyze the correct data fields as outlined in the guide. Test the workflow: Run the scenario manually to confirm that data is fetched and analyzed correctly.
by Alex Kim
Printify Automation - Update Title and Description Workflow This n8n workflow automates the process of retrieving products from Printify, generating optimized product titles and descriptions, and updating them back to the platform. It leverages OpenAI for content generation and integrates with Google Sheets for tracking and managing updates. Features Integration with Printify**: Fetch shops and products through Printify's API. AI-Powered Optimization**: Generate engaging product titles and descriptions using OpenAI's GPT model. Google Sheets Tracking**: Log and manage updates in Google Sheets. Custom Brand Guidelines**: Ensure consistent tone by incorporating brand-specific instructions. Loop Processing**: Iteratively process each product in batches. Workflow Structure Nodes Overview Manual Trigger: Manually start the workflow for testing purposes. Printify - Get Shops: Retrieves the list of shops from Printify. Printify - Get Products: Fetches product details for each shop. Split Out: Breaks down the product list into individual items for processing. Loop Over Items: Iteratively processes products in manageable batches. Generate Title and Desc: Uses OpenAI GPT to create optimized product titles and descriptions. Google Sheets Integration: Trigger: Monitors Google Sheets for changes. Log Updates: Records product updates, including old and new titles/descriptions. Conditional Logic: If Nodes: Ensure products are ready for updates and stop processing once completed. Printify - Update Product: Sends updated titles and descriptions back to Printify. Brand Guidelines + Custom Instructions: Sets brand tone and seasonal instructions. Setup Instructions Prerequisites n8n Instance: Ensure n8n is installed and configured. Printify API Key: Obtain an API key from your Printify account. Add it to n8n under HTTP Header Auth. OpenAI API Key: Obtain an API key from OpenAI. Add it to n8n under OpenAI API. Google Sheets Integration: Share your Google Sheets with the Google API service account. Configure Google Sheets credentials in n8n. Workflow Configuration Set Brand Guidelines: Update the Brand Guidelines + Custom Instructions node with your brand name, tone, and seasonal instructions. Batch Size: Configure the Loop Over Items node for optimal batch sizes. Google Sheets Configuration: Set the correct Google Sheets document and sheet names in the integration nodes. Run the Workflow: Start manually or configure the workflow to trigger automatically. Key Notes Customization**: Modify API calls to support other platforms like Printful or Vistaprint. Scalability**: Use batch processing for efficient handling of large product catalogs. Error Handling**: Configure retries or logging for any failed nodes. Output Examples Optimized Content Example Input Title**: "Classic White T-Shirt" Generated Title**: "Stylish Classic White Tee for Everyday Wear" Input Description**: "Plain white T-shirt made of cotton." Generated Description**: "Discover comfort and style with our classic white tee, crafted from premium cotton for all-day wear. Perfect for casual outings or layering." Next Steps Monitor Updates: Use Google Sheets to review logs of updated products. Expand Integration: Add support for more Printify shops or integrate with other platforms. Enhance AI Prompts: Customize prompts for different product categories or seasonal needs. Feel free to reach out for additional guidance or troubleshooting!