Workflow Templates

This workflow provides automated access to the Alitas126 Alitas2 AI model through the Replicate API. It saves you time by eliminating the need to manually interact with AI models and provides a seamless integration for other generation tasks within your n8n automation workflows. Overview This workflow automatically handles the complete other generation process using the Alitas126 Alitas2 model. It manages API authentication, parameter configuration, request processing, and result retrieval with built-in error handling and retry logic for reliable automation. Model Description: Advanced AI model for automated processing and generation tasks. Key Capabilities Specialized AI model with unique capabilities** Advanced processing and generation features** Custom AI-powered automation tools** Tools Used n8n**: The automation platform that orchestrates the workflow Replicate API**: Access to the Alitas126/alitas2 AI model Alitas126 Alitas2**: The core AI model for other generation Built-in Error Handling**: Automatic retry logic and comprehensive error management How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Replicate API: Add your Replicate API token to the 'Set API Token' node Customize Parameters: Adjust the model parameters in the 'Set Other Parameters' node Test the Workflow: Run the workflow with your desired inputs Integrate: Connect this workflow to your existing automation pipelines Use Cases Specialized Processing**: Handle specific AI tasks and workflows Custom Automation**: Implement unique business logic and processing Data Processing**: Transform and analyze various types of data AI Integration**: Add AI capabilities to existing systems and workflows Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Replicate API**: https://replicate.com (Sign up to access powerful AI models) #n8n #automation #ai #replicate #aiautomation #workflow #nocode #aiprocessing #dataprocessing #machinelearning #artificialintelligence #aitools #automation #digitalart #contentcreation #productivity #innovation

Disclaimer: This template only works on self-hosted for now, as it uses a community node. Use Case Web scrapers often break due to web page layout changes. This workflow attempts to mitigate this problem by auto-generating web scraping data extractor code via LLM. How It Works This workflow leverages ScrapeNinja n8n community node to: scrape webpage HTML, feed it into LLM (Google Gemini) and ask to write a JS extractor function code, then it executes the written JS extractor against scraped HTML to extract useful data from webpage (the code is safely executed in a sandbox) Installation To install ScrapeNinja n8n node, in your self-hosted instance, go to Settings -> Community nodes, enter "n8n-nodes-scrapeninja", and install. Make sure you are using at least v0.3.0. See this in action: https://www.linkedin.com/feed/update/urn:li:activity:7289659870935490560/

Platform: n8n (Telegram Bot Integration) Purpose: Let users fetch top meme coin prices in real-time using a simple /memecoin Telegram command How It Works (Logic Breakdown) This flow listens for a Telegram command and fetches data from the CoinGecko API to respond with live memecoin prices. 🔹 1. Telegram Trigger Node Listens for incoming Telegram messages from users. Activated when a message is sent in a Telegram chat connected to the bot. Passes the raw message (e.g., /memecoin) to the next node. 🔹 2. IF Node – Check if Message is /memecoin Condition: {{$json"message"}} === "/memecoin" If true ➝ continue to fetch data from CoinGecko. If false ➝ nothing happens. 🔹 3. HTTP Request – Fetch Meme Coins from CoinGecko API: https://api.coingecko.com/api/v3/coins/markets?...category=meme-token Fetches top 5 meme tokens by market cap. Data includes: Name Symbol Current price (USD) Coin ID (for URL linking) 🔹 4. Function Node – Format the Message Parses the JSON response from CoinGecko. Builds a clean message like: ruby Copy Edit 🚀 Dogecoin (DOGE) 💰 Price: $0.123 🔗 More: https://www.coingecko.com/en/coins/dogecoin Loops through top 5 meme coins and adds line breaks. 🔹 5. Telegram Send Node – Reply to User Sends the formatted message to the original chat. Uses chat_id from the trigger to ensure correct user receives it. 🖼 Sample User Flow 👤 User types /memecoin in Telegram bot 🤖 Bot fetches meme coin prices 📬 Bot replies with live prices + links

Modern AI systems are powerful but pose privacy risks when handling sensitive data. Organizations need AI capabilities while ensuring: ✅ Sensitive data never leaves secure environments ✅ Compliance with regulations (GDPR, HIPAA, PCI, SOX) ✅ Real-time decision making about data sensitivity ✅ Comprehensive audit trails for regulatory review The Concept: Intelligent Data Classification + Smart Routing The goal of this concept is to build the foundations of the safe and compliant use of LLMs in Agentic workflows by automatically detecting sensitive data, applying sanitization rules, and intelligently routing requests through secure processing channels. This workflow will analyze the user's chat or webhook input and attempt to detect PII using the Enhanced PII Pattern Detector. If detected, the workflow will process that input via a series of Compliance, Auditing, and Security steps which log and sanitizes the request prior to any LLM being pinged. Why Multi-Tier Routing? Traditional systems use binary decisions (sensitive/not sensitive). Our 3-tier approach provides: ✅ Granular Security: Critical PII gets maximum protection ✅ Performance Optimization: Clean data gets full cloud capabilities ✅ Cost Efficiency: Expensive local processing only when needed ✅ User Experience: Maintains conversational flow across security levels Why Context-Aware Detection? Regex patterns alone miss contextual sensitivity. Our approach: ✅ Catches Intent: "Bank account" discussion is sensitive even without account numbers ✅ Reduces False Negatives: Medical discussions stay secure even without explicit medical IDs ✅ Proactive Protection: Identifies sensitive contexts before PII is shared ✅ Compliance Alignment: Matches how regulations actually define sensitive data Why Risk Scoring vs Binary Classification? Binary PII detection creates artificial boundaries. Risk scoring provides: ✅ Nuanced Decisions: Multiple low-risk patterns might aggregate to high risk ✅ Adaptive Thresholds: Organizations can adjust sensitivity based on their needs ✅ Better UX: Users aren't unnecessarily restricted for low-risk scenarios ✅ Audit Transparency: Clear reasoning for every routing decision Why Comprehensive Monitoring? Privacy systems require trust and verification: ✅ Compliance Proof: Audit trails demonstrate regulatory compliance ✅ Performance Optimization: Identify bottlenecks and improve efficiency ✅ Security Validation: Ensure no sensitive data leakage occurs ✅ Operational Insights: Understand usage patterns and system health How to Install: All that you will need for this workflow are credentials for your LLM providers such as Ollama, OpenRouter, OpenAI, Anthropic, etc. This workflow is customizable and allows the user to define the best LLM and storage/memory solutions for their specific use case.

Effortlessly integrate NixGuard API into your n8n workflows for real-time security insights using your API key. This connector enables seamless interaction with Nix, providing rapid Retrieval-Augmented Generation (RAG) event knowledge with Wazuh integration - completely free and set up in under 5 minutes! 🚀 Features: ✅ Query NixGuard's AI-driven security insights via API authentication ✅ Real-time security event knowledge integration ✅ Plug-and-play workflow trigger for effortless automation ✅ Wazuh compatibility for full security visibility 🛠 How to Use: 1️⃣ Add your API Key to authenticate with NixGuard. 2️⃣ Integrate with your existing n8n workflows using the workflow trigger (default enabled). 3️⃣ (Optional) Activate the chat trigger to streamline security queries via chat-based inputs. 4️⃣ Run the workflow and get instant security intelligence! 📢 Perfect for: Startup CTO's, SOC teams, security engineers, and developers needing real-time security automation within their infrastructure. 🔗 Learn more about NixGuard: thenex.world 🔗 Get started with a free security subscription: thenex.world/security/subscribe

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automatically analyzes purchase trends and consumer behavior patterns to identify market opportunities and optimize business strategies. It saves you time by eliminating the need to manually analyze sales data and provides insights into buying patterns, seasonal trends, and customer preferences. Overview This workflow automatically scrapes e-commerce platforms, marketplace data, and sales analytics to extract purchase trends, product popularity, and consumer behavior insights. It uses Bright Data to access sales data and AI to intelligently analyze purchasing patterns, seasonal trends, and market opportunities. Tools Used n8n**: The automation platform that orchestrates the workflow Bright Data**: For scraping e-commerce and marketplace platforms without being blocked OpenAI**: AI agent for intelligent purchase trend analysis and forecasting Google Sheets**: For storing purchase trend data and analysis results How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Bright Data: Add your Bright Data credentials to the MCP Client node Set Up OpenAI: Configure your OpenAI API credentials Configure Google Sheets: Connect your Google Sheets account and set up your trend analysis spreadsheet Customize: Define target marketplaces and trend analysis parameters Use Cases E-commerce Strategy**: Identify trending products and market opportunities Product Development**: Understand consumer preferences and demand patterns Marketing Planning**: Optimize campaigns based on seasonal purchase trends Business Intelligence**: Make data-driven decisions using market trend insights Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #purchasetrends #marketanalysis #brightdata #webscraping #ecommerce #n8nworkflow #workflow #nocode #trendanalysis #consumerinsights #marketresearch #salesanalytics #businessintelligence #markettrends #customerinsights #ecommerceanalysis #salesdata #marketforecasting #consumerdata #purchaseanalysis #retailanalytics #marketinsights #demandforecasting #salestrends #consumertrends #marketintelligence #buyingpatterns #marketdemand

This automated n8n workflow qualifies B2B leads via voice calls using the VAPI API and integrates the collected data into Google Sheets. It triggers when a new lead’s phone number is added, streamlining lead qualification and data capture. What is VAPI? VAPI is an API service that enables voice call automation, used here to qualify leads by capturing structured data through interactive calls. Good to Know VAPI API calls may incur costs based on usage; check VAPI pricing for details. Ensure Google Sheets access is properly authorized to avoid data issues. Use credential fields for the HTTP Request node 'Bearer token' instead of hardcoding. Use a placeholder Google Sheet document ID (e.g., "your-sheet-id-placeholder") to avoid leaking private data. How It Works Detect when a new phone number is added for a lead using the New Lead Captured node. Use the Receive Lead Details from VAPI node to capture structured data (name, company, challenges) via a POST request. Trigger an outbound VAPI call to qualify the lead with the Initiate Voice Call (VAPI) node. Store the collected data into a Google Sheet using the Save Qualified Lead to CRM Sheet node. Send a success response back to VAPI with the Send Call Data Acknowledgement node. How to Use Import the workflow into n8n. Configure VAPI API credentials in the HTTP Request node using credential fields. Set up Google Sheets API access and authorize the app. Create a Google Sheet with the following columns: Name (text), Company (text), Challenges (text), Date (date). Test with a sample lead phone number to verify call initiation and data storage. Adjust the workflow as needed and retest. Requirements VAPI API credentials Google Sheets API access Customizing This Workflow Modify the Receive Lead Details from VAPI node to capture additional lead fields or adjust call scripts for specific industries.

This workflow provides automated access to the Black Forest Labs Flux Krea Dev AI model through the Replicate API. It saves you time by eliminating the need to manually interact with AI models and provides a seamless integration for image generation tasks within your n8n automation workflows. Overview This workflow automatically handles the complete image generation process using the Black Forest Labs Flux Krea Dev model. It manages API authentication, parameter configuration, request processing, and result retrieval with built-in error handling and retry logic for reliable automation. Model Description: An opinionated text-to-image model from Black Forest Labs in collaboration with Krea that excels in photorealism. Creates images that avoid the oversaturated "AI look". Key Capabilities High-quality image generation from text prompts** Advanced AI-powered visual content creation** Customizable image parameters and styles** Text-to-image transformation capabilities** Tools Used n8n**: The automation platform that orchestrates the workflow Replicate API**: Access to the Black Forest Labs/flux-krea-dev AI model Black Forest Labs Flux Krea Dev**: The core AI model for image generation Built-in Error Handling**: Automatic retry logic and comprehensive error management How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Replicate API: Add your Replicate API token to the 'Set API Token' node Customize Parameters: Adjust the model parameters in the 'Set Image Parameters' node Test the Workflow: Run the workflow with your desired inputs Integrate: Connect this workflow to your existing automation pipelines Use Cases Content Creation**: Generate unique images for blogs, social media, and marketing materials Design Prototyping**: Create visual concepts and mockups for design projects Art & Creativity**: Produce artistic images for personal or commercial use Marketing Materials**: Generate eye-catching visuals for campaigns and advertisements Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Replicate API**: https://replicate.com (Sign up to access powerful AI models) #n8n #automation #ai #replicate #aiautomation #workflow #nocode #imagegeneration #aiart #texttoimage #visualcontent #aiimages #generativeart #flux #machinelearning #artificialintelligence #aitools #automation #digitalart #contentcreation #productivity #innovation

Automate Drafts From Google Drive This workflow automates the end-to-end process of extracting and summarizing information from PDFs stored in a specific Google Drive folder. When a new PDF or any binary data is added, the workflow is triggered and begins by downloading and processing the PDF to extract all available text. If multiple PDFs are detected, their content is aggregated into a single, combined dataset. This automation eliminates the time consuming task of manually reading, taking notes, and drafting documents. By removing this burden, users can focus on more meaningful tasks while the workflow handles the repetitive, tedious work. The extracted content is then passed through an AI-powered information extractor that identifies key details such as names, dates, addresses, and any other structured data points the user wants to extract from the PDF. This step is highly customizable, allowing the user to define exactly what type of information should be extracted. While the workflow is designed to extract all available content from the PDF, specifying additional structured data points ensures that critical details are accurately captured. A second OpenAI Node uses the extracted information to draft a professional, formal summary suitable for documentation. This is the most important part of the workflow and can be fully customized to meet the user's specific needs. By editing the prompts, users can tailor the workflow to generate a wide variety of draft formats based on the extracted content. The workflow then generates a new Google Document containing the full draft and composes an email summarizing the key points in 3 to 5 bullet points. This email is automatically sent to the designated recipient along with a direct link to the Google Doc. This solution is ideal for insurance, legal, or administrative use cases where timely, accurate extraction and reporting from incoming PDFs is essential. How To Use The Workflow Step 1 - Place any binary data (e.g., PDF files) into the designated Google Drive folder. Step 2 - The workflow will automatically download each PDF, extract the text, and if multiple PDFs are present combine them into a single dataset for analysis. Step 3 - The OpenAI Draft Agent will analyze the extracted information, generate a formal draft, and create a Google Document. This document will be updated with the draft content and saved back into the same Google Drive folder. Step 4 - An email will be sent to the designated recipient(s), including a summary of the draft and key extracted information, along with a link to view the Google Document. Need Help? Have Questions? For consulting and support, or if you have questions, please feel free to connect with me on LinkedIn or email michael.gullo@outlook.com.

Description This powerful n8n automation template enables seamless synchronization between Zoho Inventory and Supabase—keeping your product database up to date with zero manual effort. Whether you’re running an eCommerce store, inventory dashboard, or product catalog app, this workflow ensures your data pipeline stays clean, consistent, and fully automated. What This Template Does: 🔁 Runs on a schedule to fetch inventory data from Zoho 🔓 Authenticates via OAuth using refresh token for secure API access 📦 Fetches products & variants with complete metadata 🔄 Splits each item and maps it into Supabase row-by-row 📊 Pushes rich product data, including name, SKU, unit, tags, stock levels, dimensions, and up to 3 custom attributes Fields Included in Sync: Product ID, Variant ID, Variant Name, Brand, SKU Returnability, Item Type, Unit, Attributes (1–3) Tags, Stock on Hand, UPC/EAN/ISBN, Status Reorder Level, Dimensions, Created Time, and more Requirements: Zoho Inventory API access (with Refresh Token) Supabase account & API key Target table (e.g., Fairy Frills) set up in Supabase Optional: Custom field mapping for additional use cases Perfect For: Inventory managers syncing Zoho to custom dashboards D2C brands and eCommerce platforms powered by Supabase Internal tooling teams needing a real-time product database sync Startups replacing spreadsheets with a production-grade backend

Important: This workflow uses the Autype community node and requires a self-hosted n8n instance. This workflow downloads a fillable PDF form from a URL, extracts all form field names and types using Autype, sends the field list to an AI Agent (OpenAI) together with applicant data, and uses the AI response to fill the form automatically. The AI is instructed to return raw JSON only, and a Code node validates the response before filling. The filled PDF is flattened (non-editable) and saved to Google Drive. Who is this for? Companies that regularly submit the same types of PDF form applications -- permit renewals, tax filings, compliance questionnaires, insurance claims, customs declarations, or any recurring government/regulatory paperwork. Instead of manually filling the same form fields every quarter or year, the AI reads the form structure and fills it with the correct data automatically. Concrete example: A manufacturing company must renew its operating permit every year by submitting a multi-page PDF application to the local regulatory authority. The form asks for company name, registration number, address, contact person, business type, employee count, and more. With this workflow, the company stores its data once in the AI Agent prompt, and every renewal period they simply run the workflow to get a completed, flattened PDF ready for submission. This also works as an additional skill for an AI agent. Instead of a manual trigger, connect the workflow to a webhook or chat trigger so an agent can call it when a user asks "fill out the permit renewal form for Q2 2026." What this workflow does On manual trigger, the workflow fetches a fillable PDF from a URL (e.g. a government portal, internal document server, or S3 bucket). It uploads the PDF to Autype and calls Get Form Fields to extract every field name, type (text, checkbox, dropdown, radio), current value, available options, and read-only status. The field list is passed directly to an AI Agent via an inline expression (no separate prompt-building Code node needed). The AI's system message instructs it to return only raw JSON. A Code node validates and parses the response before Autype fills the form, flattens it, and the result is saved to Google Drive. Showcase How it works Run Workflow -- Manual trigger starts the pipeline. Download PDF Form -- An HTTP Request node fetches the fillable PDF from a URL (the sample uses a registration form with 7 fields). Upload PDF Form -- Uploads the PDF binary to Autype Tools to get a file ID. Get Form Fields -- Autype extracts all form fields and returns them as metadata. Each field includes: name, type (text/checkbox/dropdown/radio/optionlist), value (current), options (for dropdowns/radio), and isReadOnly. No output file is created. AI Agent -- Receives the field list and applicant data directly in its prompt via an n8n expression. The system message instructs the AI to return only a raw JSON object mapping field names to values (strings for text/dropdown/radio, booleans for checkboxes). Prepare Fill Data -- A Code node parses and validates the AI JSON response (strips markdown fences if present), then pairs it with the Autype file ID. Fill PDF Form -- Autype fills every form field with the AI-generated values. Fields are flattened (non-editable) so the output is a clean, final PDF. Save Filled PDF to Drive -- The completed form is uploaded to Google Drive as filled-form-YYYY-MM-DD.pdf. Setup Install the Autype community node (n8n-nodes-autype) via Settings > Community Nodes. Create an Autype API credential with your API key from app.autype.com. See API Keys in Settings. Create an OpenAI API credential with your key from platform.openai.com. Create a Google Drive OAuth2 credential and connect your Google account. Import this workflow and assign your credentials to each node (including the OpenAI Chat Model sub-node). The sample form URL is pre-configured. To use your own form, replace the URL in the "Download PDF Form" node. Edit the applicant data directly in the AI Agent node prompt (the "Prompt (User Message)" field). Set YOUR_FOLDER_ID in the "Save Filled PDF to Drive" node to your target Google Drive folder. Click Test Workflow to run the pipeline. Note: This is a community node, so you need a self-hosted n8n instance to use community nodes. Requirements Self-hosted n8n instance (community nodes are not available on n8n Cloud) Autype account with API key (free tier available) n8n-nodes-autype community node installed OpenAI API key (gpt-4o-mini or any chat model) Google Drive account with OAuth2 credentials (optional, can replace with other output) How to customize Change applicant data:** Edit the prompt text directly in the "AI Agent" node. Replace the example person/company info with your own. Use a different AI model:** Swap the OpenAI Chat Model sub-node for Anthropic Claude, Google Gemini, or any LangChain-compatible chat model. Connect to an AI agent:** Replace the Manual Trigger with a Webhook or Chat Trigger so an AI agent can call this workflow as a tool (e.g. "fill the Q2 permit renewal form"). Skip flattening:** Set flatten to false in the "Fill PDF Form" node if you want the fields to remain editable after filling. Add watermark:** Insert an Autype Watermark step after Fill Form to stamp "DRAFT" or "SUBMITTED" on every page before saving. Add password protection:** Insert an Autype Protect step after filling to encrypt the PDF before uploading to Drive. Change output destination:** Replace the Google Drive node with Email (SMTP), S3, Slack, or any other n8n output node. Pull data from a database:** Instead of hardcoding data in the AI Agent prompt, query a database (Postgres, MySQL, Airtable) or CRM (HubSpot, Salesforce) to dynamically fill different forms for different entities.

Who's it for This workflow is ideal for AI developers running multi-agent systems in n8n who need to quantitatively evaluate tool usage behavior. If you're building autonomous agents and want to verify their decisions against ground-truth expectations, this workflow gives you plug-and-play observability. What it does This template uses n8n's built-in Evaluation Trigger and Evaluation nodes to assess whether an AI agent correctly used all the expected tools. It supports: Dataset-driven testing of agent behavior Logging actual tools to compare them with the expected tools Assigning performance metrics (tool_called = true/false) Persisting output back to Google Sheets for further debugging The workflow can be triggered by either the chat input or the dataset row evaluation. It routes through a multi-tool agent node powered by the best LLMs. The agent has access to tools such as web search, calculator, vector search, and summarizer tools. The workflow then aims to validate tool use decisions by extracting the intermediate steps from the agent (i.e., action + observation) and comparing the tools that were called with the expected tools. If the tools that were called during the workflow execution match, then it's a pass; otherwise, it's documented as a fail. The evaluation nodes take care of that process.  How to set it up Connect your Google Sheets OAuth2 credential. Replace the document with your own test dataset. Set your desired models and configure the different agent tools, such as the summarizer and vector store. The default vector store used is Qdrant, so the user must create this vector store with a few samples of queries + web search results. Run from either the chat trigger or the evaluation trigger to test. Requirements Google Sheets OAuth2 credential OpenRouter / OpenAI credentials for AI agents and embeddings Firecrawl and Qdrant credentials for web + vector search How to customize Edit the Search Agent system message to define tool selection behavior Add more metric columns in the Evaluation node for complex scoring Add new tool nodes and link them to the agent block Swap in your own summarizer