Workflow Templates

Are you a visual thinker working with n8n? 🎨 View and understand workflow structures at a glance with this template! Built with mermaid.js, Bootstrap 5 and AXAJ to create an interactive web page displaying n8n workflows as flowcharts. 🌟 Perfect for documentation, presentations, or just getting a clearer picture of your automation processes. Need customization help? Reach out to Eduard! Benefits 📊 Instant workflow visualization 📱 Responsive design 🔗 Direct links to n8n workflows 🧩 Special shapes for different node types 🚫 Disabled node indication 🔒 No external dependencies – just paste the workflow and call the webhook 🛠️ Easily customizable – enhance the JS script or add custom styling ⚠️ Important note for cloud users ⚠️ Since the cloud version doesn't support environmental variables, please make the following changes in the CONFIG node: Update the instance_url variable: Enter your n8n URL instead of {{$env["N8N_PROTOCOL"]}}://{{$env["N8N_HOST"]}} Change the webhook_path to simply "webhook" instead of {{$env["N8N_ENDPOINT_WEBHOOK"] || "webhook"}} 🌟 Examples Multiple flowcharts on a single page: Several shapes for different nodes: Langchain nodes with special connections styling:

This n8n workflow illustrates how to convert PDF files into text with the PDF Toolkit from www.customjs.space. @custom-js/n8n-nodes-pdf-toolkit Notice Community nodes can only be installed on self-hosted instances of n8n. What this workflow does Change** the requested HTML to PDF.. Extract** text from the PDF. Use** a Code node to handle URLs that point to PDF files. Convert** the PDF to text. Requirements Self-hosted** n8n instance. CustomJS API key** for converting PDF to text. HTML** Data to convert PDF files. Code node** for handling URL that indicates PDF file. Workflow Steps: Manual Trigger: Runs with user interaction. HTML to PDF: Request HTML Data Convert HTML to PDF Convert PDF to Text: Convert the generated Text from PDF Usage Get API key from customJS Sign up to customJS platform. Navigate to your profile page Press "Show" button to get API key Set Credentials for CustomJS API on n8n Copy and paste your API key generated from CustomJS here. Design workflow A Manual Trigger for starting workflow. HTTP Request Nodes for downloading PDF files. Code node for handling URL that indicates PDF file. Convert PDF to Text. You can replace logic for triggering and returning results. For example, you can trigger this workflow by calling a webhook and get a result as a response from webhook. Simply replace Manual Trigger and Write to Disk nodes.

Overview This workflow exposes an HTTP endpoint (webhook) that accepts a JSON definition of an n8n workflow, validates it, and—if everything is correct—dynamically creates that workflow in the n8n instance via its internal API. If any validation fails or the API call encounters an error, an explanatory message with details is returned. Workflow Diagram Webhook │ ▼ Validate JSON ── fails validation ──► Validation Error │ └─ passes ─► Validation Successful? │ ├─ true ─► Create Workflow ──► API Successful? ──► Success Response │ │ │ └─ false ─► API Error └─ false ─► Validation Error Step-by-Step Details 1. Webhook Type**: Webhook (POST) Path**: /webhook/create-workflow Purpose**: Expose a URL to receive a JSON definition of a workflow. Expected Input**: JSON containing the main workflow fields (name, nodes, connections, settings). 2. Validate JSON Type**: Code Node (JavaScript) Validations Performed**: Ensure that payload exists and contains both name and nodes. Verify that nodes is an array with at least one item. Check that each node includes the required fields: id, name, type, position. If missing, initialize connections, settings, parameters, and typeVersion. Output if Error**: { "success": false, "message": "<error description>" } Output if Valid**: { "success": true, "apiWorkflow": { "name": payload.name, "nodes": payload.nodes, "connections": payload.connections, "settings": payload.settings } } 3. Validation Successful? Type**: IF Node Condition**: $json.success === true Branches**: true: proceed to Create Workflow false: route to Validation Error 4. Create Workflow Type**: HTTP Request (POST) URL**: http://127.0.0.1:5678/api/v1/workflows Authentication**: Header Auth with internal credentials Body**: The apiWorkflow object generated earlier Options**: continueOnFail: true (to handle failures in the next IF) 5. API Successful? Type**: IF Node Condition**: $response.statusCode <= 299 Branches**: true: proceed to Success Response false: route to API Error 6. Success Response Type**: SET Node Output**: { "success": "true", "message": "Workflow created successfully", "workflowId": "{{ $json.data[0].id }}", "workflowName": "{{ $json.data[0].name }}", "createdAt": "{{ $json.data[0].createdAt }}", "url": "http://localhost:5678/workflow/{{ $json.data[0].id }}" } 7. API Error Type**: SET Node Output**: { "success": "false", "message": "Error creating workflow", "error": "{{ JSON.stringify($json) }}", "statusCode": "{{ $response.statusCode }}" } 8. Validation Error Type**: SET Node Output**: { "success": false, "message": "{{ $json.message }}" } Example Webhook Request curl --location --request POST 'http://localhost:5678/webhook/create-workflow' \ --header 'Content-Type: application/json' \ --data-raw '{ "name": "My Dynamic Workflow", "nodes": [ { "id": "start-node", "name": "Start", "type": "n8n-nodes-base.manualTrigger", "typeVersion": 1, "position": [100, 100], "parameters": {} }, { "id": "set-node", "name": "Set", "type": "n8n-nodes-base.set", "typeVersion": 1, "position": [300, 100], "parameters": { "values": { "string": [ { "name": "message", "value": "Hello from a webhook-created workflow!" } ] } } } ], "connections": { "Start": { "main": [ [ { "node": "Set", "type": "main", "index": 0 } ] ] } }, "settings": {} }' Expected Success Response { "success": "true", "message": "Workflow created successfully", "workflowId": "abcdef1234567890", "workflowName": "My Dynamic Workflow", "createdAt": "2025-05-31T12:34:56.789Z", "url": "http://localhost:5678/workflow/abcdef1234567890" } Validation Error Response { "success": false, "message": "The 'name' field is required in the workflow" } API Error Response { "success": "false", "message": "Error creating workflow", "error": "{ ...full API response details... }", "statusCode": 401 }

🧾 Template: Extract Ad Creatives from Google’s Ads Transparency Center This n8n workflow pulls ad creatives from Google's Ads Transparency Center using SerpApi, filtered by a specific domain and region. It extracts, filters, categorizes, and exports ads into neatly formatted CSV files for easy analysis. 👤 Who’s it for? Marketing Analysts** researching competitive PPC strategies Ad Intelligence Teams** monitoring creatives from specific brands Digital Marketers** gathering visual and copy trends Journalists & Watchdogs** reviewing ad activity transparency ✅ Features Fetch creatives** using SerpApi's google_ads_transparency_center engine Filter results** to include only ads with an exact match to your target domain Categorize** by ad format: text, image, or video Export CSVs**: Generates a downloadable file for each format under the /files/ directory 🛠 How to Use Edit the “Set Domain & Region” node domain: e.g. example.com region: SerpApi numeric region code → See codes Add your SerpApi API key In the “Get Ads Page 1” node’s credentials section. Run the workflow Click "Test workflow" to initiate the process. Download your results Navigate to /files/ to find: text_{domain}_ads.csv image_{domain}_ads.csv video_{domain}_ads.csv 📌 Notes Only the first page (up to 50 creatives) is fetched; pagination is not included. Sticky Notes inside the workflow nodes offer helpful internal annotations. CSV files include creative-level details: ad copy, images, video links, etc.

The Stripe API does not provide custom fields in invoice or charge data. So you have to get it from the Checkout Sessions endpoint. But that endpoint is not easy for begginners. It has dictionary parameters and pagination settings. This workflows solves that problem by having a preconfigured GET request that gets all the checkout sessions from the last 7 days. It then transforms the data to make it easier to work with and allows you to filter by the custom_fields you want to get. Want to generate Stripe invoices automatically? Open 👉 this workflow . Check out my other templates https://n8n.io/creators/solomon/

Automatically enrich prospect data from HubSpot using Explorium and create leads in Salesforce This n8n workflow streamlines the process of enriching prospect information by automatically pulling data from HubSpot, processing it through Explorium's AI-powered tools, and creating new leads in Salesforce with enhanced prospect details. Credentials Required To use this workflow, set up the following credentials in your n8n environment: HubSpot Type**: App Token (or OAuth2 for broader compatibility) Used for**: triggering on new contacts, fetching contact data Explorium API Type**: Generic Header Auth Header**: Authorization Value**: Bearer YOUR_API_KEY Get explorium api key Salesforce Type**: OAuth2 or Username/Password Used for**: creating new lead records Go to Settings → Credentials, create these three credentials, and assign them in the respective nodes before running the workflow. Workflow Overview Node 1: HubSpot Trigger This node listens for real-time events from the connected HubSpot account. Once triggered, the node passes metadata about the event to the next step in the flow. Node 2: HubSpot This node fetches contact details from HubSpot after the trigger event. Credential**: Connected using a HubSpot App Token Resource**: Contact Operation**: Get Contact Return All**: Disabled This node retrieves the full contact details needed for further processing and enrichment. Node 3: Match prospect This node sends each contact's data to Explorium's AI-powered prospect matching API in real time. Method**: POST Endpoint**: https://api.explorium.ai/v1/prospects/match Authentication**: Generic Header Auth (using a configured credential) Headers**: Content-Type: application/json The request body is dynamically built from contact data, typically including: full_name, company_name, email, phone_number, linkedin. These fields are matched against Explorium's intelligence graph to return enriched or validated profiles. Response Output: total_matches, matched_prospects, and a prospect_id. Each response is used downstream to enrich, validate, or create lead information. Node 4: Filter This node filters the output from the Match prospect step to ensure that only valid, matched results continue in the flow. Only records that contain at least one matched prospect with a non-null prospect_id are passed forward. Status: Currently deactivated (as shown by the "Deactivate" label) Node 5: Extract Prospect IDs from Matched Results This node extracts all valid prospect_id values from previously matched prospects and compiles them into a flat array. It loops over all matched items, extracts each prospect_id from the matched_prospects array and returns a single object with an array of all prospect_ids. Node 6: Explorium Enrich Contacts Information This node performs bulk enrichment of contacts by querying Explorium with a list of matched prospect_ids. Node Configuration: Method**: POST Endpoint**: https://api.explorium.ai/v1/prospects/contacts_information/bulk_enrich Authentication**: Header Auth (using saved credentials) Headers**: "Content-Type": "application/json", "Accept": "application/json" Returns enriched contact information, such as: emails**: professional/personal email addresses phone_numbers**: mobile and work numbers professions_email, **professional_email_status, mobile_phone Node 7: Explorium Enrich Profiles This additional enrichment node provides supplementary contact data enhancement, running in parallel with the primary enrichment process. Node 8: Merge This node combines multiple data streams from the parallel enrichment processes into a single output, allowing you to consolidate data from different Explorium enrichment endpoints. The "combine" setting indicates it will merge the incoming data streams rather than overwriting them. Node 9: Code - flatten This custom code node processes and transforms the merged enrichment data before creating the Salesforce lead. It can be used to: Flatten nested data structures Format data according to Salesforce field requirements Apply business logic or data validation Map Explorium fields to Salesforce lead properties Handle data type conversions Node 10: Salesforce This final node creates new leads in Salesforce using the enriched data returned by Explorium. Credential**: Salesforce OAuth2 or Username/Password Resource**: Lead Operation**: Create Lead The node creates new lead records with enriched information including contact details, company information, and professional data obtained through the Explorium enrichment process. Workflow Flow Summary Trigger: HubSpot webhook triggers on new/updated contacts Fetch: Retrieve contact details from HubSpot Match: Find prospect matches using Explorium Filter: Keep only successfully matched prospects (currently deactivated) Extract: Compile prospect IDs for bulk enrichment Enrich: Parallel enrichment of contact information through multiple Explorium endpoints Merge: Combine enrichment results Transform: Flatten and prepare data for Salesforce (Code node) Create: Create new lead records in Salesforce This workflow ensures comprehensive data enrichment while maintaining data quality and providing a seamless integration between HubSpot prospect data and Salesforce lead creation. The parallel enrichment structure maximizes data collection efficiency before creating high-quality leads in your CRM system.

This Workflow does a HTTPs request to ConnectWise Manage through their REST API. It will pull all tickets in the "New" status or whichever status you like, and notify your dispatch team/personnel whenever a new ticket comes in using Microsoft Teams. Video Explanation https://youtu.be/yaSVCybSWbM

Who’s it for This template is perfect for content creators, AI enthusiasts, marketers, and developers who want to automate the generation of cinematic videos using Google Vertex AI’s Veo 3 model. It’s also ideal for anyone experimenting with generative AI for video using n8n. What it does This workflow: Accepts a text prompt and a GCP access token via form. Sends the prompt to the Veo 3 (preview model) using Vertex AI’s predictLongRunning endpoint. Waits for the video rendering to complete. Fetches the final result and converts the base64-encoded video to a file. Uploads the resulting .mp4 to your Google Drive. Output How to set up Enable Vertex AI API in your GCP project: https://console.cloud.google.com/marketplace/product/google/aiplatform.googleapis.com Authenticate with GCP using Cloud Shell or local terminal: gcloud auth login gcloud config set project [YOUR_PROJECT_ID] gcloud auth application-default set-quota-project [YOUR_PROJECT_ID] gcloud auth print-access-token Copy the token and use it in the form when running the workflow. ⚠️ This token lasts ~1 hour. Regenerate as needed. Connect your Google Drive OAuth2 credentials to allow file upload. Import this workflow into n8n and execute it via form trigger. Requirements n8n (v1.94.1+)** A Google Cloud project with: Vertex AI API enabled Billing enabled A way to get Access Token A Google Drive OAuth2 credential connected to n8n How to customize the workflow You can modify the in the HTTP node to match your use case. Replace the Google Drive upload node with alternatives like Dropbox, S3, or YouTube upload. Extend the workflow to add subtitles, audio dubbing, or LINE/Slack alerts. Step-by-step for each major node: Prompt Input → Vertex Predict → Wait → Fetch Result → Convert to File → Upload Best Practices Followed No hardcoded API tokens Secure: GCP token is input via form, not stored in workflow All nodes are renamed with clear purpose All editable config grouped in Set node External References GCP Veo API Docs: https://cloud.google.com/vertex-ai/docs/generative-ai/video/overview Disclaimer This workflow uses official Google Cloud APIs and requires a valid GCP project. Access token should be generated securely using gcloud CLI. Do not embed tokens in the workflow itself. Notes on GCP Access Token To use the Vertex AI API in n8n securely: Run the following on your local machine or GCP Cloud Shell: gcloud auth login gcloud config set project your-project-id gcloud auth print-access-token Paste the token in the workflow form field when submitting. Do not hardcode the token into HTTP nodes or Set nodes — input it each time or use a secure credential vault.

Temporary solution using the undocumented REST API for backups using Google drive. Please note that there are issues with this workflow. It does not support versioning, so please know that it will create multiple copies of the workflows so if you run this daily it will make the folder grow quickly. Once I figure out how to version in Gdrive I'll update it here.

This workflow demonstrates how to can use Redis to implement rate limits to your API. The workflow uses the incoming API key to uniquely identify the user and use it as a key in Redis. Every time a request is made, the value is incremented by one, and we check for the threshold using the IF node. Duplicate the following Airtable to try out the workflow: https://airtable.com/shraudfG9XAvqkBpF

-- Disclaimer: This template is mainly made for self-hosted users who can reach CSV files in their file system. For Cloud users, just replace the first few nodes with your file system of choice, like Google Drive or Dropbox -- How to automatically import CSV files into postgres 1、project description This workflow demonstrates how CSV file can be automatically imported into existing PostgreSQL database. Before running the workflow please make sure you have a file on the server: /tmp/t1.csv The name of the test database is db01, and you can replace it. then create table t1 create table t1(id int,name varchar(10)); And the content of the file is the following: |id|name| |-|-|-| |1|a| |2|b| |3|c| 2、Other If you want to import a custom csv file, please refer to the following methods. 2.1、Create a table in the database SQL Commands: https://www.postgresql.org/docs/current/sql-createtable.html 2.2、Upload csv file Upload csv file to N8N server and make sure it can be read.

AI News Scraping System This n8n workflow automates the process of pulling in breaking AI-related headlines from curated RSS feeds, scraping their full content, and saving readable Markdown versions directly to Google Drive. Use cases include: Creating a personal newsletter curation system Automating blog post research workflows Archiving news content for later summarization or AI use How it Works Scheduled Triggers The workflow runs every 3–4 hours using multiple Schedule Trigger nodes. Each trigger targets a different news source (e.g., Google News, OpenAI Blog, Hugging Face, etc.). Fetch and Parse Feeds RSS feeds are fetched via the HTTP Request node. Items from the feed are split into individual entries using the Split Out node. Scrape Article Content Each article URL is sent to the Firecrawl API with a prompt to extract only the main content in Markdown. The scraping skips navigation, headers, footers, and ads. Convert and Save The extracted Markdown is converted into a .md file using the Convert to File node. The file is then uploaded to a Google Drive folder. Good to Know This workflow uses the Firecrawl API for web scraping. Be sure to configure a Generic HTTP Header credential with your API key. Output files are saved in Markdown format You can add more Schedule Trigger + HTTP Request pairs to extend this workflow to additional feeds. Requirements Firecrawl API account for scraping Google Drive account (OAuth2 credentials must be configured in n8n) n8n instance (self-hosted or cloud) Customization Ideas Replace or extend RSS feeds with sources relevant to your niche Load up scraped news stories into a prompt to create new content like TikToks and Reels Add a summarization step using an LLM like GPT or Claude Send the Markdown files to Notion, Slack, or a blog CMS Example Feeds | Feed Name | URL | |------------------|----------------------------------------------------------------------| | Google News (AI) | https://rss.app/feeds/v1.1/AkOariu1C7YyUUMv.json | | OpenAI Blog | https://rss.app/feeds/v1.1/xNVg2hbY14Z7Gpva.json | | Hugging Face | https://rss.app/feeds/v1.1/sgHcE2ehHQMTWhrL.json |