Workflow Templates

🚀 What the “Agent Builder” template does Need to turn a one-line chat request into a fully-wired n8n workflow template—complete with AI agents, RAG, and web-search super-powers—without lifting a finger? That’s exactly what Agent Builder automates: Listens to any incoming chat message (via the Chat Trigger). Spins up an AI architect that analyses the request, searches the web, reads n8n docs from a Pinecone vector store, and designs the smallest possible set of nodes. Auto-generates a ready-to-import JSON template and hands it back as a downloadable file—plus all the supporting assets (embeddings, vector store etc.) so the next prompt is even smarter. Think of it as your personal “workflow chef”: you shout the order, it shops for ingredients, cooks, plates, and serves the meal. All you do is eat. 🤗 Who will love this? No-code builders / power users** who don’t want to wrestle with AI node wiring. Agencies & consultants** delivering lots of bespoke automations. Internal platform teams** who need a “workflow self-service portal” for non-technical colleagues. 🧩 How it’s wired | Sub-process | What happens inside | Key nodes | | -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------- | | Web Crawler (optional) | Firecrawl scrapes docs.n8n.io (or any URL you drop in) and streams raw markdown back. | Set URL → HTTP Request (Extract) → Wait & Retry | | RAG Trainer | Splits the scraped docs, embeds them with OpenAI, and upserts vectors into Pinecone. | Recursive Text Splitter → Embeddings OpenAI → Train Pinecone | | Agent Builder | The star of the show – orchestrates GPT-4o (via OpenRouter), SerpAPI web-search, your Pinecone index and a Structured Output Parser to produce → validate → prettify the final n8n template. | Chat Trigger → AI Agent → OpenAI (validator) → Code (extract) → Convert to JSON file | Every arrow in the drawn workflow is pre-connected, so the generated template always passes n8n’s import check. 🛠️ Getting set up (5 quick creds) | Service | Credential type | | --------------------------------------------------- | ---------------------------------------------------------- | | OpenAI / Azure OpenAI – embeddings & validation | OpenAI API | | Pinecone – vector store | Pinecone API | | OpenRouter – GPT-4o LLM | OpenRouter API Key | | SerpAPI – web search | SerpAPI Key | | Firecrawl (only if you plan to crawl) | Generic Header Auth → Authorization: Bearer YOUR_KEY | Each node already expects those creds; just create them once, select in the dropdown, hit Activate. 🏃‍♀️ What a typical run looks like User says: “Build me a workflow that monitors our support inbox, summarises new tickets with GPT and posts to Slack.” Chat Trigger captures the message. AI Agent: queries Pinecone for relevant n8n docs, fires a SerpAPI search for “n8n gmail trigger example”, sketches an architecture (Gmail Trigger → GPT Model → Slack). The agent returns JSON ➜ OpenAI node double-checks field names, connections, type versions. A tiny JS Code node slices the JSON out of the chat blob and saves it as template.json ready for download. You download, import, and… done. ✏️ Customising Switch the LLM* – plug in Claude 3, Gemini 1.5, or a local model; just swap the *OpenRouter Chat Model node. Point the RAG at your own docs* – change the crawl URL or feed PDFs via the *Default Data Loader. Hard-code preferred nodes* – edit the “User node preferences” in the system message so the agent always chooses *Notion for databases, etc. 🥡 Take-away notes It's a prototype feel free to experiment with it to improve its capabilities. Have fun building!**

🧾 Personal Invoice Processor This N8N workflow automates the extraction and organization of personal invoices in Colombia received via Gmail. It includes the following key steps: 🔁 Flow Summary Email Trigger Polls Gmail every 30 minutes for emails with .zip attachments (assumed to contain invoices). Expects ZIP file following DIAN standards. ZIP File Handling Extracts all files. Filters only PDF and XML files for processing. Data Extraction & Processing Uses LangChain Agent + OpenAI (GPT-4o-mini) to extract: Tipo de documento (Factura / Nota Crédito) Número de factura Fecha de emisión (YYYY-MM-DD) NIT emisor y receptor (sin dígito de verificación) Razón social del emisor Subtotal, IVA, Total CUFE Resumen de compra (max 20 words, formatted sentence) Validation Ensures Total = Subtotal + IVA using a calculator node. Storage Uploads the original PDF to Google Drive. Renames the file to: YYYY-MM-DD-NUMERO_FACTURA.pdf. Inserts or updates invoice details in Google Sheets using a unique Key (NIT_Emisor + Numero_Factura) to prevent duplication. > ⚙️ Designed for personal use with minimal latency tolerance and high automation reliability.

LLM/RAG Kaggle Development Assistant An on-premises, domain-specific AI assistant for Kaggle (tested on binary disaster-tweet classification), combining LLM, an n8n workflow engine, and Qdrant-backed Retrieval-Augmented Generation (RAG). Deploy via containerized starter kit. Needs high end GPU support or patience. Initial chat should contain guidelines on what to to produce and the challenge guidelines. Features Coding Assistance** • "Real"-time Python code recommendations, debugging help, and data-science best practices • Multi-turn conversational context Workflow Automation** • n8n orchestration for LLM calls, document ingestion, and external API integrations Retrieval-Augmented Generation (RAG)** • Qdrant vector-database for competition-specific document lookup • On-demand retrieval of Kaggle competition guidelines, tutorials, and notebooks after convertion to HTML and ingestion into RAG entirly On-Premises for Privacy** • Locally hosted LLM (via Ollama) – no external code or data transfer ALIENTELLIGENCE/contentsummarizer:latest for summarizing qwen3:8b for chat and coding mxbai-embed-large:latest for embedding • GPU acceleration required Based on: https://n8n.io/workflows/2339 breakdown documents into study notes using templating mistralai and qdrant/

What This Workflow Does This workflow automates the candidate nurturing process, solving the common problem of candidates losing interest or "ghosting" after an application. It keeps them engaged and informed by sending a personalized, multi-channel (WhatsApp & Gmail) sequence of follow-up messages over their first week. The automation triggers when a new candidate is added to your ATS (e.g., via a Recrutei webhook). It then uses AI to generate a custom 3-part message (for Day 1, Day 3, and Day 7) tailored to the candidate's age and the specific job they applied for, ensuring a professional and empathetic experience that strengthens your employer brand. How it Works Trigger: A Webhook node captures the new candidate data from your Applicant Tracking System (ATS) or form. Data Preparation: Two Code nodes clean the incoming data. The first (Separating information) extracts key fields and formats the phone number. The second (Extract age) calculates the candidate's age from their birthday to be used by the AI. AI Content Generation: The workflow sends the candidate's details (name, age, job title) to an AI model (AI Recruitment Assistant). The AI has a detailed system prompt to generate three distinct messages for Day 1 (Thank You), Day 3 (Friendly Reminder), and Day 7 (Final Reinforcement), adapting its tone based on the candidate's age. Split Messages: A Code node (Separating messages per days) receives the single text block from the AI and splits it into three separate variables (day1, day3, day7). Day 1 Send: The workflow immediately sends the day1 message via both Gmail and WhatsApp (configured for Evolution API). Day 3 Send: A "Wait" node pauses the workflow for 2 days, after which it sends the day3 message. Day 7 Send: Another "Wait" node pauses for 4 more days, then sends the final day7 message, completing the 7-day nurturing sequence. Setup Instructions This workflow is plug-and-play once you configure the following 5 steps: Webhook Node: Copy the Test URL from the Webhook node and configure it in your ATS (e.g., Recrutei) or form builder to trigger whenever a new candidate is added. Run one test submission to make the data structure visible to n8n. AI Credentials: In the AI Recruitment Assistant node, select or create your OpenAI API credential. MCP Credential (Optional): If you use a Recrutei MCP, paste your endpoint URL into the MCP Recrutei node. Gmail Credentials: In all three Message Gmail nodes (Day 1, 3, 7), select or create your Gmail (OAuth2) credential. Optional: In the same nodes, go to Options and change the Sender Name from your_company to your actual company name. WhatsApp (Evolution API): This template is pre-configured for the Evolution API. In all three Message WhatsApp nodes (Day 1, 3, 7), you must: URL: Replace {server-url} and {instance} with your Evolution API details. Headers: In the "Header Parameters" section, replace your_api_key with your actual Evolution API key.

This workflow integrates both web scraping and NLP functionalities. It uses HTML parsing to extract links, HTTP requests to fetch essay content, and AI-based summarization using GPT-4o. It's an excellent example of an end-to-end automated task that is not only efficient but also provides real value by summarizing valuable content. Note that to use this template, you need to be on n8n version 1.50.0 or later.

This n8n workflow simplifies the process of removing backgrounds from images stored in Google Drive. By leveraging the PhotoRoom API, this template enables automatic background removal, padding adjustments, and output formatting, all while storing the updated images back in a designated Google Drive folder. This workflow is very useful for companies or individuals that are spending a lot of time into removing the background from product images. How it Works The workflow begins with a Google Drive Trigger node that monitors a specific folder for new image uploads. Upon detecting a new image, the workflow downloads the file and extracts essential metadata, such as the file size. Configurations are set for background color, padding, output size, and more, which are all customizable to match specific requirements. The PhotoRoom API is called to process the image by removing its background and adding padding based on the settings. The processed image is saved back to Google Drive in the specified output folder with an updated name indicating the background has been removed. Requirements PhotoRoom API Key Google Drive API Access Customizing the Workflow Easily adjust the background color, padding, and output size using the configuration node. Modify the output folder path in Google Drive or replace Google Drive with another storage service if needed. For advanced use cases, integrate further image processing steps, such as adding captions or analyzing content using AI.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automatically tracks customer satisfaction scores across multiple platforms and surveys to help improve customer experience and identify areas for enhancement. It saves you time by eliminating the need to manually check different feedback sources and provides comprehensive satisfaction analytics. Overview This workflow automatically scrapes customer satisfaction surveys, review platforms, and feedback forms to extract satisfaction scores and sentiment data. It uses Bright Data to access various feedback platforms without being blocked and AI to intelligently analyze satisfaction trends and identify improvement opportunities. Tools Used n8n**: The automation platform that orchestrates the workflow Bright Data**: For scraping satisfaction surveys and review platforms without being blocked OpenAI**: AI agent for intelligent satisfaction analysis and trend identification Google Sheets**: For storing satisfaction scores and generating analytics reports 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 satisfaction tracking spreadsheet Customize: Define feedback sources and satisfaction metrics you want to monitor Use Cases Customer Experience**: Monitor satisfaction trends across all customer touchpoints Product Teams**: Identify product features that impact customer satisfaction Support Teams**: Track satisfaction scores for support interactions Management**: Get comprehensive satisfaction reporting for strategic decisions 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 #customersatisfaction #satisfactionscores #brightdata #webscraping #customerexperience #n8nworkflow #workflow #nocode #satisfactiontracking #csat #nps #customeranalytics #feedbackanalysis #customerinsights #satisfactionmonitoring #experiencemanagement #customermetrics #satisfactionsurveys #feedbackautomation #customerfeedback #satisfactiondata #customerjourney #experienceanalytics #satisfactionreporting #customersentiment #experienceoptimization #satisfactiontrends #customervoice

Overview This workflow automatically converts CSV or Excel files into a production-ready database schema using AI and rule-based validation. It analyzes uploaded data, detects column types, relationships, and data quality, then generates a normalized schema. The output includes SQL DDL scripts, ERD diagrams, a data dictionary, and a load plan. This eliminates manual schema design and accelerates database setup from raw data. How It Works File Upload (Webhook) Accepts CSV or XLSX files via webhook endpoint Initializes workflow configuration (thresholds, retry limits) File Extraction Detects file format (CSV or Excel) Extracts rows into structured JSON Merges extracted datasets Data Cleaning & Profiling Removes duplicates and normalizes values Detects data types (integer, float, date, boolean, string) Computes column statistics (nulls, uniqueness, distributions) Generates file hash and sample dataset Column Profiling Engine Identifies potential primary keys Detects cardinality and uniqueness levels Suggests foreign key relationships based on value overlap AI Schema Generation Uses an AI agent to design normalized tables Assigns SQL data types based on real data Defines primary keys, foreign keys, constraints, and indexes Validation Layer Ensures schema matches actual data Validates: Data types Primary key uniqueness Foreign key overlap (>70%) Constraint consistency Detects circular dependencies Revision Loop If validation fails: Sends feedback to AI agent Regenerates schema Retries up to configured limit Schema Output Generation Generates: SQL DDL scripts ERD (Mermaid format) Data dictionary Load plan with dependency graph Load Plan Engine Computes optimal table insertion order Detects circular dependencies Suggests batching strategy Combine & Explain Merges all outputs Optional AI explanation of schema decisions Response Output Returns structured JSON via webhook: SQL schema ERD summary Data dictionary Load plan Optional explanation Setup Instructions Activate the workflow and copy the webhook URL Send a POST request with a CSV or XLSX file Configure OpenAI credentials (used by AI agent) Adjust thresholds if needed (FK overlap, retries, confidence) Execute workflow and review generated outputs Use Cases Auto-generate database schema from CSV/Excel files Data migration and onboarding pipelines Rapid database prototyping Reverse engineering datasets AI-assisted data modeling Requirements n8n (latest version recommended) OpenAI API credentials LangChain nodes enabled CSV or XLSX input file

Automatically converts CSV/XLSX files into a fully validated database schema using AI, generating SQL scripts, ERD diagrams, a data dictionary, and load plans to accelerate database design and data onboarding. EXPLANATION This workflow automates the end-to-end process of transforming raw CSV or Excel data into a production-ready relational database schema. It begins by accepting file uploads through a webhook, detecting file type, and extracting structured data. The workflow performs data cleaning and deep profiling to analyze column types, uniqueness, null values, and patterns. A column analysis engine identifies candidate primary keys and potential relationships. An AI agent then generates a normalized schema by organizing data into tables, assigning appropriate SQL data types, and defining primary and foreign keys. The schema is validated using rule-based checks to ensure data integrity, correct relationships, and proper normalization. If validation fails, the workflow automatically refines the schema through a revision loop. Once validated, it generates SQL DDL scripts, ERD diagrams, a data dictionary, and a load plan that determines the correct order for inserting data. Finally, all outputs are combined and returned via webhook as a structured response, making the workflow ideal for rapid database creation, data migration, and AI-assisted data modeling. Overview This workflow automatically converts CSV or Excel files into a production-ready relational database schema using AI and rule-based validation. It analyzes uploaded data to detect column types, relationships, and data quality, then generates a normalized schema with proper keys and constraints. The output includes SQL DDL scripts, ERD diagrams, a data dictionary, and a load plan. This eliminates manual schema design and accelerates database setup from raw data. How It Works File Upload (Webhook) Accepts CSV or XLSX files and initializes workflow configuration such as thresholds and retry limits. File Extraction Detects file format and extracts rows into structured JSON format. Data Cleaning & Profiling Cleans data, removes duplicates, normalizes values, and computes column statistics such as null percentage and uniqueness. Column Analysis Engine Identifies candidate primary keys, analyzes cardinality, and suggests potential foreign key relationships. AI Schema Generation Uses an AI agent to design normalized tables, assign SQL data types, and define primary keys, foreign keys, and constraints. Validation Layer Validates schema integrity by checking data types, primary key uniqueness, foreign key overlap, and constraint consistency. Revision Loop If validation fails, the workflow sends feedback to the AI agent and regenerates the schema until it meets requirements. Schema Output Generation Generates SQL DDL scripts, ERD diagrams, a data dictionary, and a load plan. Load Plan Engine Determines the correct order for inserting data and detects circular dependencies. Combine & Explain Merges all outputs and optionally provides AI-generated explanations of schema decisions. Response Output Returns all generated artifacts as a structured JSON response via webhook. Setup Instructions Activate the workflow and copy the webhook URL Send a POST request with a CSV or XLSX file Configure OpenAI credentials for the AI agent Adjust thresholds if needed (FK overlap, retries, confidence) Execute the workflow and review outputs Use Cases Automatically generate database schemas from CSV/Excel files Accelerate data migration and onboarding pipelines Rapidly prototype relational database designs Reverse engineer structured schemas from raw datasets AI-assisted data modeling and normalization Requirements n8n (latest version recommended) OpenAI API credentials LangChain nodes enabled CSV or XLSX input file

Intelligently Segment PDFs by Table of Contents This workflow empowers you to automatically process PDF documents, intelligently identify or generate a hierarchical Table of Contents (ToC), and then segment the entire document's content based on these ToC headings. It effectively breaks down a large PDF into its constituent sections, each paired with its corresponding heading and hierarchical level. Why It's Useful Unlock the true structure of your PDFs for granular access and advanced processing: AI Agent Tool:** A key use case is to provide this workflow as a tool to an AI agent. The agent can then use the segmented output to "read" and navigate to specific sections of a document to answer questions, extract information, or perform tasks with much greater accuracy and efficiency. Targeted Content Extraction:** Programmatically pull out specific chapters or subsections for focused analysis, summarization, reporting, or repurposing content. Enhanced RAG Systems:** Improve your Retrieval Augmented Generation (RAG) pipelines by feeding them well-defined, contextually relevant document sections instead of entire, monolithic PDFs. This leads to more precise AI-generated responses. Modular Document Processing:** Process different parts of a document using distinct logic in subsequent n8n workflows by acting on individual sections. Data Preparation:** Seamlessly convert lengthy PDFs into a structured format where each section (including its heading, level, and content in multiple formats) becomes a distinct, manageable item. How It Works Ingestion & Advanced Parsing: The workflow ingests a PDF (via a provided URL or a pre-set one for manual runs). It then utilizes Chunkr.ai to perform Optical Character Recognition (OCR) and parse the document into detailed structural elements, extracting text, HTML, and Markdown for each segment. AI-Powered Table of Contents Generation: A Google Gemini AI model analyzes the initial pages of the document (where a ToC often resides) along with section headers extracted by Chunkr as a fallback. This allows it to construct an accurate, hierarchical Table of Contents in a structured JSON format, even if the PDF lacks an explicit ToC or if it's poorly formatted. Precise Content Segmentation: Sophisticated custom code then meticulously maps the AI-generated ToC headings to their corresponding content within the parsed document from Chunkr. It intelligently determines the precise start and end of each section. Structured & Flexible Output: The primary output provides each identified section as an individual n8n item. Each item includes the heading text, its hierarchical level (e.g., 1, 1.1, 2), and the full content of that section in Text, HTML, and Markdown formats. Optionally, the workflow can also reconstruct the entire document into a single, navigable HTML file or a clean Markdown file. What You Need To run this workflow, you'll need: Input PDF:** When triggered by another workflow: A URL pointing to the PDF document. When triggered manually: The workflow uses a pre-configured sample PDF from Google Drive for demonstration (this can be customized). Chunkr.ai API Key:** Required for the initial parsing and OCR of the PDF document. You'll need to insert this into the relevant HTTP Request nodes. Google Gemini API Credentials:** Necessary for the AI model to intelligently generate the Table of Contents. This should be configured in the Google Gemini Chat Model nodes. Outputs The workflow primarily generates: Individual Document Sections:** A series of n8n items. Each item represents a distinct section of the PDF and contains: heading: The text of the section heading. headingLevel: The hierarchical level of the heading (e.g., 1 for H1, 2 for H2). sectionText: The plain text content of the section. sectionHTML: The HTML content of the section. sectionMarkdown: The Markdown content of the section. Alternatively, you can configure the workflow to output: Full Reconstructed Document:** A single HTML file representing the entire processed document. A single Markdown file representing the entire processed document. This workflow is ideal for anyone looking to deconstruct PDFs into meaningful, manageable parts for advanced automation, AI integration, or detailed content analysis.

Overview The [n8n] YouTube Channel Advanced RSS Feeds Generator workflow facilitates the generation of various RSS feed formats for YouTube channels without requiring API access or administrative permissions. It utilizes third-party services to extract data, making it extremely user-friendly and accessible. Key Use Cases and Benefits Content Aggregation**: Easily gather and syndicate content from any public YouTube channel. No API Key Required**: Avoid the complexities and limitations of Google's API. Multiple Formats**: Supports ATOM, JSON, MRSS, Plaintext, Sfeed, and direct YouTube XML feeds. Flexibility**: Input can be a YouTube channel or video URL, ID, or username. Services/APIs Utilized This workflow integrates with: commentpicker.com**: For retrieving YouTube channel IDs. rss-bridge.org**: To generate various RSS formats. Configuration Instructions Start the Workflow: Activate the workflow in your n8n instance. Input Details: Enter the YouTube channel or video URL, ID, or username via the provided form trigger. Run the Workflow: Execute the workflow to receive links to 13 different RSS feeds, including community and video content feeds. Screenshots Additional Notes Customization**: You can modify the RSS feed formats or integrate additional services as needed. Support and Contributions For support, questions, or contributions, please visit the n8n community forum or the GitHub repository. We welcome contributions from the community!

Based on your use case, you might want to trigger a workflow if new data gets added to your database. This workflow allows you to send a message to Mattermost when new data gets added in Google Sheets. The Interval node triggers the workflow every 45 minutes. You can modify the timing based on your use case. You can even use the Cron node to trigger the workflow. If you wish to fetch new Tweets from Twitter, replace the Google Sheet node with the respective node. Update the Function node accordingly.