Workflow Templates

📲 AI Multi-Model Telegram Chatbot (n8n + AIMLAPI) This n8n workflow enables Telegram users to interact with multiple AI models dynamically using #model_id commands. It also supports a /models command to list all available models. Each user has a daily usage limit, tracked via Google Sheets. 🚀 Key Features Dynamic Model Selection:** Users choose models on-the-fly via #model_id (e.g., #openai/gpt-4o). /models Command:** Lists all available models grouped by provider. Daily Limit Per User:** Enforced using Google Sheets. Prompt Parsing:** Extracts model and message from user input. Logging:** Logs every request & result into Google Sheets for usage tracking. Seamless Telegram Delivery:** Responses are sent directly back to the chat. 🛠 Setup Guide 1. 📲 Create a Telegram Bot Go to @BotFather Use /newbot → Set name & username. Copy the generated API token. 2. 🔐 Add Telegram Credentials to n8n Go to n8n > Credentials > Telegram API. Create a new credential with the BotFather token. 3. 📗 Google Sheets Setup Create a Google Sheet named Sheet1. Add columns: user_id | date | query | result Share the sheet with your Service Account or OAuth Email (depending on auth method). 4. 🔌 Connect AIMLAPI Get your API key from AIMLAPI. In n8n > Credentials, add AI/ML API: API Key: your_key_here. 5. ⚙️ Customize Limits & Enhancements Adjust daily limits in the Set Daily Limit node. Optional: Add NSFW content filtering. Implement alias commands. Extend with /help, /usage, /history. Add inline button UX (advanced). 💡 How It Works ➡️ Command Examples: Start a chat with a specific model: #openai/gpt-4o Write a motivational quote. Request available models list: /models ➡️ Workflow Logic: Receives a Telegram message. Switch node checks if the message is /models or a prompt. For /models, it fetches and sends a grouped list of models. For prompts: Checks usage limits. Parses #model_id and prompt text. Dynamically routes the request to the chosen model. Sends the AI's response back to the user. Logs the query & result to Google Sheets. If daily limit exceeded → sends a limit exceeded message. 🧪 Testing & Debugging Tips Test via a separate Telegram chat. Use Console/Set nodes to debug payloads. Always test commands by messaging the bot (not via "Execute Node"). Validate cases: Missing #model_id. Invalid model_id. Limit exceeded handling.

What it does Automates code review by listening for a comment trigger on GitLab merge requests, summarising the diff, and using an LLM to post constructive, line‑specific feedback. If a JIRA ticket ID is found in the MR description, the ticket’s summary is used to inform the AI review. Use cases Quickly obtain high‑quality feedback on MRs without waiting for peers. Highlight logic, security or performance issues that might slip through cursory reviews. Incorporate project context by pulling in related JIRA ticket summaries. Good to know Triggered by commenting ai-review on a merge request. The LLM returns only high‑value findings; if nothing critical is detected, the workflow posts an “all clear” message. You can swap out the LLM (Gemini, OpenAI, etc.) or adjust the prompt to fit your team’s guidelines. AI usage may incur costs or be geo‑restricted depending on your provider n8n.io. How it works Webhook listener:** A Webhook node captures GitLab note events and filters for the trigger phrase. Fetch & parse:** The workflow retrieves MR details and diffs, splitting each change into “original” and “new” code blocks. Optional JIRA context:** If your MR description includes a JIRA key (e.g., PROJ-123), the workflow fetches the ticket (and parent ticket for subtasks) and composes a brief context summary. LLM review:** The parsed diff and optional context are sent to an LLM with instructions to identify logic, security or performance issues and suggest improvements. Post results:** Inline comments are posted back to the MR at the appropriate file/line positions; if no issues are found, a single “all clear” note is posted. How to use Import the template JSON and open the Webhook node. Replace the REPLACE_WITH_UNIQUE_PATH placeholder with your desired path and configure a GitLab project webhook to send MR comments to that URL. Select your LLM credentials in the Gemini (or other LLM) node, and optionally add JIRA credentials in the JIRA nodes. Activate the workflow and comment ai-review on any merge request to test it. For each review, the workflow posts status updates (“AI review initiated…”) and final comments. Requirements A GitLab project with a generate Personal Access Token (PAT) stored as an environment variable (GITLAB_TOKEN). LLM credentials (e.g., Google Gemini) and optional JIRA credentials. Customising this workflow Change the trigger phrase in the Trigger Phrase Filter node. Modify the LLM prompt to focus on different aspects (e.g., style, documentation). Filter out certain file types or directories before sending diffs to the LLM. Integrate other services (Slack, email) to notify teams when reviews are complete.

💡 How It Works This workflow automatically detects new YouTube uploads, retrieves their transcripts, summarizes them in Japanese using GPT-4 o mini, and posts the results to a selected Slack channel. It’s ideal for teams who follow multiple creators, internal training playlists, or corporate webinars and want concise Japanese summaries in Slack without manual work. Here’s the flow at a glance: YouTube RSS Trigger — monitors a specific channel’s RSS feed. HTTP Request via RapidAPI — fetches the video transcript (supports both English & Japanese). Code Node — merges segmented transcript text into one clean string. OpenAI (GPT-4o-mini) — generates a natural-sounding, 3-line Japanese summary. Slack Message — posts the title, link, and generated summary to #youtube-summary. ⚙️ Requirements n8n (v1.60 or later) RapidAPI account + [youtube-transcript3 API key] OpenAI API key (GPT-4o-mini recommended) Slack workspace with OAuth connection 🧩 Setup Instructions 1.Replace YOUR_RAPIDAPI_KEY_HERE with your own RapidAPI key. 2.Add your OpenAI Credential under Credentials → OpenAI. 3.Set your target Slack channel (e.g., #youtube-summary). 4.Enter the YouTube channel ID in the RSS Trigger node. 5.Activate the workflow and test with a recent video. 🎛️ Customization Tips Modify the OpenAI prompt to change summary length or tone. Duplicate the RSS Trigger for multiple channels → merge before summarization. Localize Slack messages using Japanese or English templates. 🚀 Use Case Perfect for marketing teams, content curators, and knowledge managers who want to stay updated on YouTube content in Japanese without leaving Slack.

Automatically research new leads in your target area, structure the results with AI, and append them into Google Sheets — all orchestrated in n8n. ✅ What this template does Uses Perplexity to research businesses (coffee shops in this example) with company name + email Cleans and structures the output into proper JSON using OpenAI Appends the new leads directly into Google Sheets, skipping duplicates > Trigger: Manual — “Start Workflow” 👤 Who’s it for Sales & marketing teams** who need to prospect local businesses Agencies** running outreach campaigns Freelancers** and consultants looking to automate lead research ⚙️ How it works Set Location → define your target area (e.g., Hershey PA) Get Current Leads → pull existing data from your Google Sheet to avoid duplicates Research Leads → query Perplexity for 20 businesses, excluding already-scraped ones Write JSON → OpenAI converts Perplexity output into structured Company/Email arrays Split & Merge → align Companies with Emails row-by-row Send Leads to Google Sheets → append or update leads in your sheet 🛠️ Setup instructions Follow these sticky-note setup steps (already included in the workflow): 1) Connect Google Sheets (OAuth2) In n8n → Credentials → New → Google Sheets (OAuth2) Sign in with your Google account and grant access In the Google Sheets node, select your Spreadsheet and Worksheet Example sheet: https://docs.google.com/spreadsheets/d/1MnaU8hSi8PleDNVcNnyJ5CgmDYJSUTsr7X5HIwa-MLk/edit#gid=0 2) Connect Perplexity (API Key) Sign in at https://www.perplexity.ai/account Generate an API key: https://docs.perplexity.ai/guides/getting-started In n8n → Credentials → New → Perplexity API, paste your key 3) Connect OpenAI (API Key) In n8n → Credentials → New → OpenAI API Paste your OpenAI API key In the OpenAI Chat Model node, select your credential and a vision-capable model (e.g., gpt-4o-mini, gpt-4o) 🔧 Requirements A free Google account An OpenAI API key (https://platform.openai.com) A Perplexity API key (https://docs.perplexity.ai) n8n self-hosted or cloud instance 🎨 How to customize Change the Search Area in the Set Location node Modify the Perplexity system prompt to target different business types (e.g., gyms, salons, restaurants) Expand the Google Sheet schema to include more fields (phone, website, etc.) 📬 Contact Need help customizing this (e.g., filtering by campaign, sending reports by email, or formatting your Google Sheet)? 📧 robert@ynteractive.com 🔗 Robert Breen 🌐 ynteractive.com

🧑‍💻 Description This workflow integrates Slack with an OpenAI Chat Agent to create a fully interactive chatbot inside your Slack workspace. It works in a bidirectional loop: A user sends a message in Slack. The workflow captures the message and logs it back into Slack (so you can monitor what’s being passed into the agent). The message is sent to an OpenAI-powered agent (e.g., GPT-4o). The agent generates a response. The response is formatted and posted back to Slack in the same channel or DM thread. This allows you to monitor, test, and interact with the agent directly from Slack. 📌 Use Cases Team Support Bot**: Provide quick AI-generated answers to FAQs in Slack. E-commerce Example**: The default prompt makes the bot act like a store assistant, but you can swap in your own domain knowledge. Conversation Monitoring**: Log both user and agent messages in Slack for visibility and review. Custom AI Agents**: Extend with RAG, external APIs, or workflow automations for specialized tasks. ⚙️ Setup Instructions 1️⃣ OpenAI Setup Sign up at OpenAI. Generate an API key from the API Keys page. In n8n → Credentials → New → OpenAI → paste your key and save. In the OpenAI Chat node, select your credential and configure the system prompt. Example included: “You are an ecommerce bot. Help the user as if you were working for a mock store.” You can edit this prompt to fit your use case (support bot, HR assistant, knowledge retriever, etc.). 2️⃣ Slack Setup Go to Slack API Apps → click Create New App. Under OAuth & Permissions, add the following scopes: Read: channels:history, groups:history, im:history, mpim:history, channels:read, groups:read, users:read. Write: chat:write. Install the app to your workspace → copy the Bot User OAuth Token. In n8n → Credentials → New → Slack OAuth2 API → paste the token and save. In the Slack nodes (e.g., Send User Message in Slack, Send Agent’s Response in Slack), select your credential and specify the Channel ID or User ID to send/receive messages. 🎛️ Customization Guidance Change Agent Behavior: Update the system message in the **Chat Agent node. Filter Channels**: Limit listening to a specific channel by adjusting the Slack node’s Channel ID. Format Responses: The **Format Response node shows how to structure agent replies before posting back to Slack. Extend Workflows**: Add integrations with databases, CRMs, or APIs for dynamic data-driven responses. 🔄 Workflow Flow (Simplified) Slack User Message → Send User Message in Slack → Chat Agent → Format Response → Send Agent Response in Slack 📬 Contact Need help customizing this workflow (e.g., multi-channel listening, advanced AI logic, or external integrations)? 📧 robert@ynteractive.com 🔗 Robert Breen 🌐 ynteractive.com

Description: Transform your health data into actionable meal plans with an Advanced AI Chain. 🥗🤖 This workflow goes beyond a simple prompt. It orchestrates a chain of Google Gemini agents to manage your blood pressure. It acts as a personal health assistant that analyzes your data, strategizes a diet plan, and finds real-world recipes. Key Features: Dual AI Logic: Uses Gemini (1.5-flash) in two stages—first to decide the search strategy based on BP status (High/Normal), and second to synthesize a 5-day meal plan. Real Recipe Search: Automatically searches Google for recipes that match the AI's dietary strategy (e.g., "Low sodium dinner"). Visual Tracking: Generates a blood pressure trend chart using QuickChart.io and attaches it to the email report. Organized Layout: Nodes are clearly grouped into sections (Data Collection, AI Strategy, Execution, Synthesis) for easy customization. How it works: Analyze: Fetches last 7 days of BP data from Google Sheets. Decide: AI determines the best search keywords (e.g., "Dash diet recipes") based on your average BP. Execute: Searches for recipes and generates a chart simultaneously. Synthesize: AI combines the recipes and health stats into a weekly plan. Deliver: Emails the plan and chart to you. Setup Requirements: Google Sheets: Create headers: date, systolic, diastolic. Google Gemini API Key Google Custom Search API Key & Engine ID Gmail

Analyze Furusato Nozei trends from Google News to Slack This workflow acts as a specialized market analyst for Japan's "Furusato Nozei" (Hometown Tax) system. It automates the process of monitoring related news, validating keyword popularity via search trends, and delivering a concise, strategic report to Slack. By combining RSS feeds, AI agents, and real-time search data, this template helps marketers and municipal researchers stay on top of the highly competitive Hometown Tax market without manual searching. 👥 Who is this for? Municipal Government Planners:** To track trending return gifts and competitor strategies. E-commerce Marketers:** To identify high-demand keywords for Furusato Nozei portals. Content Creators:** To find trending topics for blogs or social media regarding tax deductions. Market Researchers:** To monitor the seasonality and shifting interests in the Hometown Tax sector. ⚙️ How it works News Ingestion: The workflow triggers on a schedule and fetches the latest "Furusato Nozei" articles from Google News via RSS. AI Analysis & Extraction: An AI Agent (using OpenRouter) summarizes the news cluster and identifies the most viable search keyword (e.g., "Scallops," "Travel Vouchers," or specific municipalities). Data Validation: The workflow queries the Google Trends API (via SerpApi) to retrieve search volume history for the extracted keyword in Japan. Strategic Reporting: A second AI Agent analyzes the search trend data alongside the keyword to generate a market insight report. Delivery: The final report is formatted and sent directly to a Slack channel. 🛠️ Requirements To use this workflow, you will need: n8n** (Version 1.0 or later recommended). OpenRouter API Key** (or you can swap the model nodes for OpenAI/Anthropic). SerpApi Key** (Required to fetch Google Trends data programmatically). Slack Account** (with permissions to post to a channel). 🚀 How to set up Configure Credentials: Add your OpenRouter API key to the Chat Model nodes. Add your SerpApi key to the Google Trends API node. Connect your Slack account in the Send a message node. Check the RSS Feed: The RSS Read node is pre-configured for "Furusato Nozei" (ふるさと納税). You can leave this as is. Regional Settings: The workflow is pre-set for Japan (jp / ja). If you need to change this, check the Workflow Configuration and Google Trends API nodes. Schedule: Enable the Schedule Trigger node to run at your preferred time (default is 9:00 AM JST). 🎨 How to customize Change the Topic:** While this is optimized for Furusato Nozei, you can change the RSS feed URL to track other Japanese market trends (e.g., NISA, Inbound Tourism). Swap AI Models:** The template uses OpenRouter, but you can easily replace the "Chat Model" nodes with OpenAI (GPT-4) or Anthropic (Claude) depending on your preference. Adjust AI Prompts:** The AI prompts are currently in Japanese to match the content. You can modify the system instructions in the AI Agent nodes if you prefer English reports.

How it works This n8n template automates lead processing from your website. It receives customer data via a Webhook, stores the customer's contact (email or phone number) in the respective SendPulse address books, and uses the SendPulse MCP Server to send personalized welcome messages (email or SMS) generated using AI. The template also includes built-in SendPulse token management logic with caching in the Data Table, which reduces the number of unnecessary API requests. SendPulse’s MCP server is a tool that helps you manage your account through a chat with an AI assistant. It uses SendPulse API methods to get information and perform actions, such as request statistics, run message campaigns, or update user data. MCP server acts as middleware between your AI assistant and your SendPulse account. It processes requests through the SendPulse API and sends results back to chat, so you can manage everything without leaving the conversation. Once connected, the MCP server operates as follows: You ask your AI assistant something in chat. It forwards your request to the MCP server. The MCP server calls the API to get data or perform an action. The AI assistant sends the result back to your chat. Set up Requirements: An active SendPulse account. Client ID and Client Secret from your SendPulse account. An API key from your OpenAI account to power the AI agent. Set up steps: Get your OpenAI API Key - https://platform.openai.com/api-keys Add your OpenAI API Key to OpenAI Chat Model node in n8n workflow. Get your Client ID and Client Secret from your SendPulse account - https://login.sendpulse.com/settings/#api Add your Client ID and Client Secret to Workflow Configuration node. Add your Client ID and Client Secret to SendPulse MCP Client node as headers X-SP-ID і X-SP-SECRET in Multiple Headers Auth. In the Workflow Configuration node, change the names of the mailing lists, senderName, senderEmail, smsSender, routeCountryCode and routeType fileds as needed. Create a tokens table with the columns: hash (string), accessToken (string), tokenExpiry (string) in the Data tables section of your n8n platform account.

📄 What this workflow does This workflow automatically turns a topic and a reference image URL into a finished, branded article image. It uses GPT-4o to generate a short, detailed image prompt, sends it to FAL Flux image-to-image for rendering, polls until the job is completed, downloads and resizes the image, overlays your company logo, and finally saves the branded result into a specified Google Drive folder. 👤 Who is this for Content teams who need consistent, on-brand article images. Marketing teams looking to scale blog and landing page visuals. Designers who want to automate repetitive resizing and branding tasks. Anyone who needs a pipeline from topic → AI illustration → Google Drive asset. ✅ Requirements OpenAI (GPT-4o) API credentials (for image prompt generation). FAL API key for Flux image-to-image generation. Google Drive OAuth2 connection + target folder ID for saving images. A company logo file/URL (direct download link from Google Drive or any public URL). ⚙️ How to set up Connect OpenAI GPT-4o in the “Create prompt” node. Add your FAL API key to all HTTP Request nodes (generate image, check image finish, Get image link). Replace the logo link in “Get company’s logo” with your own logo URL. Configure the Google Drive node with your OAuth2 credentials and set the correct Folder ID. Update the image_url in “Link image” (or pass from upstream data). Test the workflow end-to-end with a sample subject and image. 🔁 How it works Form/Manual Trigger → Input subject + reference image URL. GPT-4o → Generates a <70-word sharp/detailed prompt (no text/logos). FAL Flux (HTTP Request) → Submits job for image-to-image generation. Polling Loop → Wait + check status until COMPLETED. Download Image → Retrieves generated image link. Resize Image → Standardize to 800×500 pixels. Get & Resize Logo → Fetch company logo, resize for branding. Composite → Overlay logo onto article image. Save to Google Drive → Final branded image saved in target folder. 💡 About Margin AI Margin AI is your AI Service Companion. We help organizations design intelligent, human-centric automation — from content pipelines and branding workflows to customer insights and sales enablement. Our tailored AI solutions scale marketing, operations, and creative processes with ease.

This template qualifies and segments B2B prospects in ProspectPro using live web data and AI. It retrieves website content and search snippets, processes them with an LLM, and updates the prospect record in ProspectPro with qualification labels and tags. The workflow ensures each prospect is processed once and can be reused as a sub-flow or direct trigger. ✨ Features Automatically qualify B2B companies based on website and search content Flexible business logic: qualify and segment prospects by your own criteria Updates ProspectPro records with labels and tags Live data retrieval via Bedrijfsdata.nl RAG API nodes Easy customization through flexible AI setup Extendable and modular: use as a trigger workflow or callable sub-flow ⚙ Requirements n8n instance or cloud workspace Install the Bedrijfsdata.nl Verified Community Node Bedrijfsdata.nl developer account (14-day free trial, 500 credits) Install the ProspectPro Verified Community Node ProspectPro account & API credentials (14-day free trial) OpenAI API credentials (or another LLM) 🔧 Setup Instructions Import the template and set your credentials (Bedrijfsdata.nl, ProspectPro, OpenAI). Connect to a trigger (e.g., ProspectPro "New website visitor") or call as a sub-workflow. Adjust qualification logic in the Qualify & Tag Prospect node to match your ICP. Optional: extend tags, integrate with Slack/CRM, or add error logging. 🔐 Security Notes Prevents re-processing of the same prospect using tags Error branches included for invalid input or API failures LLM output validated via a structured parser 🧪 Testing Run with a ProspectPro ID of a company with a known domain Check execution history and ProspectPro for enrichment results Verify updated tags and qualification label in ProspectPro 📌 About Bedrijfsdata.nl Bedrijfsdata.nl operates the most comprehensive company database in the Netherlands. With real-time data on 3.7M+ businesses and AI-ready APIs, they help Dutch SMEs enrich CRM, workflows, and marketing automation. Website: https://www.bedrijfsdata.nl Developer Platform: https://developers.bedrijfsdata.nl API docs: docs.bedrijfsdata.nl Support: https://www.bedrijfsdata.nl/klantenservice Support hours: Monday–Friday, 09:00–17:00 CET 📌 About ProspectPro ProspectPro is a B2B Prospecting Platform for Dutch B2B SMEs. It helps sales teams identify prospects, identify website visitors and more. Website: https://www.prospectpro.nl Platform: https://mijn.prospectpro.nl API docs: https://www.docs.bedrijfsdata.nl Support: https://www.prospectpro.nl/klantenservice Support hours: Monday–Friday, 09:00–17:00 CET

Who’s it for Freelancers and agencies who track new Upwork leads via Vollna RSS and want clean logging to Google Sheets with instant Slack alerts. What it does Polls a Vollna RSS feed every few minutes, parses each job (title, budget, link, skills, categories), dedupes against your sheet, appends only new jobs, and notifies Slack with a compact alert. How it works Schedule Trigger fires on an interval. RSS Read pulls new items from Vollna. Filter (optional) skips non-ASCII titles. Code node normalizes fields (title/budget split, clean Upwork link, “Posted x mins ago”, etc.). Sheets Lookup + Compare prevents duplicates by job URL. Sheets Append writes new rows; Slack posts a job alert. Set up In ⚙️ Config, set: VOLLNA_RSS_URL, GOOGLE_SHEETS_DOC_ID, GOOGLE_SHEET_NAME, SLACK_CHANNEL_ID, EMAIL_TO (optional). Add OAuth credentials for Google Sheets, Slack, and Gmail (optional). Create sheet columns: TITLE, BUDGET, UPWORK JOB LINK, CATEGORIES, SKILLS, DATE, JOB DESCRIPTION, POSTED. (Optional) Adjust polling interval on the Schedule Trigger. Requirements • Vollna RSS feed URL (your tokenized link) • n8n (cloud or self-hosted) with Google Sheets + Slack creds Customize • Remove the ASCII filter for broader coverage. • Swap Gmail/Slack with your preferred notifier. • Add keyword filters before appending to Sheets.

Personalized Learning Content Aggregator with AI Filtering Who’s it for This workflow is for learners, educators, and professionals who want to automatically collect and filter the most relevant educational articles, tutorials, and resources based on specific keywords. How it works Fetches content from RSS feeds and Reddit based on user-defined keywords. AI analyzes and filters the articles to keep only relevant, educational, and non-promotional posts. Saves curated results into a Google Sheet for easy review. How to set up Connect your Google Sheets and AI (OpenAI or LangChain) credentials. Add your RSS feed URLs and keywords to Google Sheets. Adjust schedule timing in the trigger node (default: 8 AM & 6 PM daily). Run the workflow and check the results in your Google Sheet. Requirements Google Sheets account for storage. RSS feed URLs and keyword list. AI node (OpenAI / Gemini / Claude) for filtering logic. How to customize Change or add new content sources (e.g., YouTube, Medium, Dev.to). Adjust AI prompt criteria to match your learning goals. Save results to other platforms (e.g., Notion, Slack, or Airtable). Note: This workflow uses no personal identifiers or API keys directly in nodes. All credentials are safely stored in n8n’s credential manager.