Workflow Templates

Who is this for? For developers and organizations that need to convert HTML files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Converts HTML to file. Converts the HTML file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

contract input: length of the strings and number of copies output: random strings as specified. randomness determined by Crypto node (generate/base64) How to run the workflow Import the workflow into your n8n project Click the Form Trigger to specify the length of the strings and how many copies to generate The workflow runs then displays the generated random strings How to set up No additional set up is necessary to execute the workflow manually. integration Patterns of interests formTrigger node to submit a form, then use form (end) node to display results at the end of the triggered workflow. set(dup)-summarize(concatenate) to run a part of the workflow multiple times then merge the results to one piece of data

An intelligent AI-powered agent that automatically browses publication websites, analyzes page content with natural language understanding, and identifies the latest downloadable reports, research papers, and data files across multiple sources using advanced structured output parsing. What Makes This Different: AI-Powered Content Analysis** - Uses advanced language models (GPT-4/GPT-5.1) to understand page context and identify downloadable reports, even when links aren't explicitly labeled, handling complex page layouts and dynamic content Structured Output Parsing** - Enforces JSON schema validation ensuring consistent data extraction with required fields (title, link, file_type, description), eliminating parsing errors and data inconsistencies HTML to Markdown Conversion** - Converts raw HTML to clean Markdown before AI processing, removing noise and improving AI comprehension of page structure and content hierarchy Intelligent Link Detection** - AI agent identifies direct download URLs, converts relative links to absolute URLs, and prioritizes the most recent reports based on publication dates and page positioning Comprehensive Validation** - Multi-layer validation checks link format, file type detection, and report relevance before saving, ensuring only valid, downloadable reports enter your library Flexible Source Management** - Reads publication sources from Google Sheets, enabling easy addition/removal of sources without workflow modification, with support for categories and custom metadata Key Benefits of AI-Powered Report Discovery: Automated Discovery** - Eliminates manual browsing and searching across multiple publication sites, saving hours of research time while ensuring you never miss new reports Context-Aware Extraction** - AI understands page context, distinguishing between actual reports and navigation links, category pages, or promotional content Prioritized Results** - Automatically selects the most recent and relevant report from each source, focusing on quality over quantity Structured Data Output** - All discovered reports are saved with consistent metadata (title, link, file type, description, source), making them easy to search, filter, and integrate with other systems Error Resilience** - Handles missing reports gracefully, logging when no reports are found without failing the entire workflow, ensuring continuous operation Integration Ready** - Can be called by other workflows (e.g., PDF downloader), enabling end-to-end automation from discovery to storage Who's it for This template is designed for researchers, market analysts, competitive intelligence teams, academic institutions, industry monitoring services, and anyone who needs to systematically discover and track downloadable reports from multiple publication sources. It's perfect for organizations that need to monitor industry publications, track competitor research, discover new market reports, build research libraries, or stay updated on latest publications without manually visiting dozens of websites daily. How it works / What it does This workflow creates an AI-powered report discovery system that reads publication source URLs from Google Sheets, fetches their pages, uses AI to analyze content, and extracts information about downloadable reports. The system: Reads Active Sources - Fetches publication URLs and metadata from Google Sheets "Report Sources" sheet, processing each source in sequence Loops Through Sources - Processes sources one at a time using Split in Batches, ensuring proper error isolation and preventing batch failures Fetches Publication Pages - Downloads HTML content from each source URL with proper browser headers (User-Agent, Accept, Accept-Language) to avoid blocking Converts HTML to Markdown - Transforms raw HTML into clean Markdown format, removing styling, scripts, and navigation elements to improve AI comprehension AI Analysis - LangChain agent analyzes the Markdown content using GPT-4/GPT-5.1, identifying downloadable reports based on context, link patterns, and content structure Structured Output Parsing - Enforces JSON schema validation, ensuring the AI returns data in the exact format: source, title, link, file_type, description Validates & Normalizes Output - Validates extracted links are absolute URLs, checks file type indicators, determines report validity, and normalizes all fields Routes by Validity - IF node routes valid reports to save operation, invalid/missing reports to logging Saves Discovered Reports - Appends valid reports to Google Sheets "Discovered Reports" sheet with metadata, source URL, category, and discovery timestamp Logs No Report Found - Records sources where no valid reports were found in "Discovery Log" sheet for monitoring and troubleshooting Tracks Completion - Generates completion summary with number of sources checked and processing timestamp Key Innovation: AI-Powered Context Understanding - Unlike traditional web scrapers that rely on fixed CSS selectors or regex patterns, this workflow uses AI to understand page context and semantics. The AI can identify reports even when they're embedded in complex layouts, use non-standard naming, or require understanding of surrounding text to determine relevance. This makes it adaptable to any website structure without manual configuration. How to set up 1. Prepare Google Sheets Create a Google Sheet with three tabs: "Report Sources", "Discovered Reports", and "Discovery Log" In "Report Sources" sheet, create columns: Source_Name, Source_URL, Category (optional) Add publication URLs in the Source_URL column (e.g., "https://example.com/research" or "https://publisher.com/reports") Add descriptive names in Source_Name column for easy identification Optionally add Category values (e.g., "Market Research", "Industry Reports", "Academic Papers") The "Discovered Reports" sheet will be automatically populated with columns: source, title, link, fileType, description, sourceUrl, category, discoveredAt, status, isValid The "Discovery Log" sheet will record sources where no reports were found Verify your Google Sheets credentials are set up in n8n (OAuth2 recommended) 2. Configure Google Sheets Nodes Open the "Read Active Sources" node and select your spreadsheet from the document dropdown Set sheet name to "Report Sources" Configure the "Save Discovered Report" node: select same spreadsheet, set sheet name to "Discovered Reports", operation should be "Append or Update" Configure the "Log No Report Found" node: same spreadsheet, "Discovery Log" sheet, operation "Append or Update" Test connection by running the "Read Active Sources" node manually to verify it can access your sheet 3. Set Up OpenAI Credentials Open the "OpenAI GPT-5.1" node (or configure the model you want to use) Connect your OpenAI API credentials (API key required) The workflow uses GPT-5.1 by default, but you can change to GPT-4, GPT-4 Turbo, or other models Temperature is set to 0.1 for consistent, deterministic output Verify API key has sufficient credits and access to the selected model For cost optimization, GPT-4 Turbo is recommended for similar results at lower cost 4. Configure AI Agent & Output Parser The "AI Report Discovery Agent" node contains a detailed system prompt that instructs the AI on what to look for The prompt is pre-configured but can be customized for your specific needs (e.g., prioritize certain file types, look for specific keywords) The "Structured Output Parser" enforces the JSON schema - verify the schema matches your needs: { "source": "Publisher Name", "title": "Report Title", "link": "https://example.com/report.pdf", "file_type": "pdf", "description": "Brief description" } The parser ensures the AI always returns valid JSON with all required fields Test the AI agent by manually running with a sample source URL to verify it correctly identifies reports 5. Customize Discovery Rules (Optional) The AI agent's system prompt can be modified in the "AI Report Discovery Agent" node Current rules prioritize: downloadable files (PDF, Excel, Word, PowerPoint), most recent publications, direct download URLs To customize: Edit the system message to add specific keywords, file types, or discovery patterns Example customization: Add industry-specific terms or prioritize reports with certain keywords in titles The validation code in "Validate & Normalize Output" can be adjusted to change what's considered "valid" Test with your specific sources to ensure discovery rules work as expected 6. Set Up Scheduling & Test The workflow includes Manual Trigger (for testing), Schedule Trigger (runs daily), and Execute Workflow Trigger (for calling from other workflows) To customize schedule: Open "Schedule (Daily)" node and adjust interval (e.g., twice daily, weekly) For initial testing: Use Manual Trigger, add 2-3 test publication URLs to your "Report Sources" sheet Verify execution: Check that pages are fetched, AI analysis completes, and reports are saved to "Discovered Reports" Monitor execution logs: Check for API errors, timeout issues, or parsing failures Review Discovery Log: Verify sources with no reports are properly logged Common issues: OpenAI API rate limits (add delays if processing many sources), invalid URLs (check source URLs), timeout errors (increase timeout for slow-loading pages), AI not finding reports (may need to adjust system prompt for specific site structures) Requirements OpenAI API Key** - Active OpenAI account with API access and sufficient credits for GPT-4/GPT-5.1 model usage (API key configured in n8n credentials) Google Sheets Account** - Active Google account with OAuth2 credentials configured in n8n for reading and writing spreadsheet data Source Spreadsheet** - Google Sheet with "Report Sources", "Discovered Reports", and "Discovery Log" tabs, properly formatted with required columns Valid Publication URLs** - Direct links to publication pages that contain downloadable reports (not direct PDF links - the workflow discovers those) n8n Instance** - Self-hosted or cloud n8n instance with access to external websites (HTTP Request node needs internet connectivity) and LangChain nodes enabled

This workflow demonstrates two ways of exporting data from SQL to XML. First, several random records are received from the MySQL database. Then, in the upper part of the workflow, the structure of an XML is defined in the Set node. After that, the ItemLists node combines all items into an array. This allows an XML node to create a simple XML file. The lower part of the workflow shows how to create an XML with attributes. It is almost identical except that a $ (dollar sign) JSON key is used to define XML attributes. Finally, both files are saved locally.

Run Apache Airflow DAG and Retrieve XCom Value What this workflow does This workflow integrates the Apache Airflow API DAGRun and XCom. It enables n8n to trigger Airflow DAGs and retrieve the execution results. Preparation: Update Airflow API Link Prefix Navigate to the airflow-api node. Update the prefix of the Airflow API link in the format: http(s)://ip:port. Example: https://airflow.example.com Configure Authentication Go to the Airflow: dag_run node. Update the Basic Auth credentials with your Airflow username and password. Repeat this step for Airflow: dag_run - state and Airflow: dag_run - get result nodes. Security Note: Using Basic Authentication requires storing credentials in plaintext. If possible, consider using API Keys or Tokens for enhanced security. An example is setting Airflow's API Authentication to basic\_auth. Choose other authentication methods if needed. Ensure the user account has the following permissions: can create on DAG Runs, can read on DAG Runs, can read on XComs, can edit on DAGs, and can read on DAGs. How to Use: To execute this workflow, use the Execute Sub-workflow node with the following input parameters: dag\_id**: The DAG ID (name) in Airflow that you want to trigger. task\_id**: The Task ID (name) from which you want to retrieve the XCom return\_value. conf**: Input data for the Airflow DAG run. wait**: Delay (in seconds) between each Airflow: dag_run - state check. wait\_time**: The maximum time (in seconds) to wait for Airflow: dag_run - state before returning an error. Output: The workflow returns the XCom result from Airflow: dag_run - get result. The XCom return_value is stored in the value field.

This workflow collects images from web search results on a specific query, analyzes the image for labels, formats the text, and adds the information in Google Sheets. HTTP Request node** gets images from the web. AWS Rekognition node** analyzes the images (in this case, it detects text in an image). Set node** sets the values necessary for the data set. Function node** transforms the text detected in the image to lower case. Google Sheets node** adds the image information to a sheet that serves as data set.

This workflow will allow you to enrich in real-time a form submission from Webflow using Datagma. Based on the result of this workflow, a specific Calendly link will be shown on the website. If the process outcome is '1', a link for a one-on-one demo will be provided. If the process outcome is '2', a link for a group demo will be shown. Full guide here: Real-time Lead Routing

Luma Photon Flash Image Generator Description Accelerated variant of Photon prioritizing speed while maintaining quality Overview This n8n workflow integrates with the Replicate API to use the luma/photon-flash model. This powerful AI model can generate high-quality image content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters prompt** (string): Text prompt for image generation Optional Parameters seed** (integer, default: None): Random seed. Set for reproducible generation aspect_ratio** (string, default: 16:9): Aspect ratio of the generated image image_reference** (string, default: None): Reference image to guide generation style_reference** (string, default: None): Style reference image to guide generation character_reference** (string, default: None): Character reference image to guide generation image_reference_url** (string, default: None): Deprecated: Use image_reference instead style_reference_url** (string, default: None): Deprecated: Use style_reference instead image_reference_weight** (number, default: 0.85): Weight of the reference image. Larger values will make the reference image have a stronger influence on the generated image. style_reference_weight** (number, default: 0.85): Weight of the style reference image character_reference_url** (string, default: None): Deprecated: Use character_reference instead How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate image content Access the generated output from the final node API Reference Model: luma/photon-flash API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

Fire V Sekai.mediapipe Labeler Image Generator Description Mediapipe Blendshape Labeler - Predicts the blend shapes of an image. Overview This n8n workflow integrates with the Replicate API to use the fire/v-sekai.mediapipe-labeler model. This powerful AI model can generate high-quality image content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters media_path** (string): Input image, video, or training zip file Optional Parameters test_mode** (boolean, default: False): Enable test mode for quick verification max_people** (integer, default: 100): Maximum number of people to detect (1-100) export_train** (boolean, default: True): Export training zip containing json annotations and frame pngs aligned_media** (string, default: None): Optional video that is aligned with the input video's annotations frame_sample_rate** (integer, default: 1): Process every nth frame for video input How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate image content Access the generated output from the final node API Reference Model: fire/v-sekai.mediapipe-labeler API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

Restore All n8n Workflows from Google Drive Backups Restoring multiple n8n workflows manually, especially when migrating your n8n instance to another host or server, can be an incredibly daunting and time-consuming task. Imagine having to individually export and then manually import hundreds of workflows; it's a recipe for errors and significant downtime. This workflow provides a streamlined way to restore all your n8n workflows from backup JSON files stored in a designated Google Drive folder. It's an essential tool for disaster recovery, migrating workflows to a new n8n instance, or recovering from accidental deletions, ideally used in conjunction with a backup solution like our "Auto Backup Workflows To Google Drive" template. For more powerful n8n templates, visit our website or contact us at AI Automation Pro. We help your business build custom AI workflow automation and apps. Who is this for? This template is intended for: n8n Users and Administrators:** Who have previously backed up their n8n workflows as JSON files to Google Drive. Anyone needing to recover their n8n setup:** Whether due to system failure, data corruption, accidental deletions, or during an instance migration. What problem is this workflow solving? / use case Restoring multiple n8n workflows manually can be a slow and error-prone process. This workflow solves that by: Automating Bulk Restore:** Quickly re-imports all workflows from a specified Google Drive backup folder, drastically cutting down on manual effort. Disaster Recovery:** Enables rapid recovery of your automation environment, minimizing downtime after a system failure or data corruption. Simplified Instance Migration:** Makes the process of transferring your entire workflow suite to a new n8n server significantly more manageable and less error-prone compared to manual imports. Data Integrity:** Helps restore workflows to a known good state from your backups, ensuring consistency after a recovery or migration. What this workflow does Manual Trigger: You initiate the workflow manually whenever a restore operation is needed. List Backup Files: The workflow accesses a specific Google Drive folder (which you must configure) and lists all the files within it. It assumes these are your n8n workflow JSON backup files. Iterate and Process: It then loops through each file found in the Google Drive folder: Download Workflow: Downloads the individual workflow JSON file from Google Drive. Extract Content: Parses the downloaded file to extract the JSON data representing the workflow. Import to n8n: Uses the n8n API to create a new workflow (or update an existing one if an ID match is found) in your current n8n instance using the extracted JSON data. Wait Step: Pauses for 3 seconds after attempting to create each workflow to help manage system load and avoid potential API rate-limiting issues. Step-by-step setup Import Template: Upload the provided JSON file into your n8n instance. Configure Credentials: Google Drive Nodes: You will need to create or select existing Google Drive OAuth2 API credentials for these nodes. n8n Node: Configure your n8n API credentials to allow the workflow to create/update workflows in your instance. Specify Google Drive Backup Folder (CRITICAL): Open the "Google Drive Get All Workflows" node. Locate the "Filter" section, and within it, the "Folder ID" parameter. The default value is a placeholder URL. You MUST change this URL to the direct URL of the Google Drive folder that contains your n8n workflow .json backup files. This would typically be one of the hourly folders (e.g., n8n_backup_YYYY-MM-DD_HH) created by the companion backup workflow. Activate Workflow: Although manually triggered, the workflow needs to be active in your n8n instance to be runnable. How to customize this workflow to your needs Selective Restore:** Option 1 (Manual): Before running the workflow, manually move only the specific workflow JSON files you want to restore into the source Google Drive folder configured in the "Google Drive Get All Workflows" node. Option 2 (Automated Filter): Insert an "Edit Fields" or "Filter" node after the "Google Drive Get All Workflows" node to programmatically select which files (e.g., based on filename patterns) should proceed to the "Loop Over Items" node for restoration. Adjust Wait Time:** The "Wait" node is set to 3 seconds. You can increase this if you have a very large number of workflows or if your n8n instance requires more time between API calls. Conversely, for smaller batches on powerful instances, you might decrease it. Error Handling:** For enhanced robustness, consider adding error handling branches (e.g., using "Error Trigger" nodes or "Continue on Fail" settings within nodes) to log or send notifications if a specific workflow fails to import. Important Considerations Workflow Overwriting/Updating:* If a workflow with the same id as one in a backup JSON file already exists in your n8n instance, this restore process will typically *update/overwrite** that existing workflow with the version from the backup. If the id from the backup file does not correspond to any existing workflow, a new workflow will be created. Idempotency:** Running this workflow multiple times on the exact same backup folder will cause the workflow to re-process all files. This means workflows will be updated/overwritten again if they exist, or created if they don't. Ensure this is the intended behavior. Companion Backup Workflow:** This restore workflow is ideally paired with backups created by a process like our "Auto Backup Workflows To Google Drive" template, which saves workflows in the expected JSON format. Test Safely:** It's highly recommended to test this workflow on a non-production or development n8n instance first, especially when restoring a large number of critical workflows or if you're unsure about the overwrite behavior in your specific n8n setup. Source Folder Content:* Ensure the specified Google Drive folder *only contains n8n workflow JSON files that you intend to restore. Other file types may cause errors in the "Extract from File" node.

This workflow allows you to collect tweets, store them in MongoDB, analyse their sentiment, insert them into a Postgres database, and post positive tweets in a Slack channel. Cron node: Schedule the workflow to run every day Twitter node: Collect tweets MongoDB node: Insert the collected tweets in MongoDB Google Cloud Natural Language node: Analyse the sentiment of the collected tweets Set node: Extract the sentiment score and magnitude Postgres node: Insert the tweets and their sentiment score and magnitude in a Posgres database IF node: Filter tweets with positive and negative sentiment scores Slack node: Post tweets with a positive sentiment score in a Slack channel NoOp node: Ignore tweets with a negative sentiment score

> ⚠️ Disclaimer: This workflow uses Scrapeless and Claude AI via community nodes, which require n8n self-hosted to work properly. 🔁 How It Works This intelligent B2B lead generation workflow combines search automation, website crawling, AI analysis, and multi-channel output: It starts by using Scrapeless’s Deep Serp API to find company websites from targeted Google Search queries. Each result is then individually crawled using Scrapeless's Crawler module, retrieving key business information from pages like /about, /contact, /services. The raw web content is processed via a Code node to clean, extract, and prepare structured data. The cleaned data is passed to Claude Sonnet (Anthropic) which analyzes and qualifies the lead based on content richness, contact data, and relevance. A filter step ensures only high-quality leads (e.g. lead score ≥ 6) are kept. Sent via Discord webhook for real-time notification (can be replaced with Slack, email, or CRM tools). > 📌 The result is a fully automated system that finds, qualifies, and organizes B2B leads with high efficiency and minimal manual input. ✅ Pre-Conditions Before using this workflow, make sure you have: An n8n self-hosted instance A Scrapeless account and API key (get it here) An Anthropic Claude API key A configured Discord webhook URL (or alternative notification service) ⚙️ Workflow Overview Manual Trigger → Scrapeless Google Search → Item Lists → Scrapeless Crawler → Code (Data Cleaning) → Claude Sonnet → Code (Response Parser) → Filter → Discord Notification 🔨 Step-by-Step Breakdown Manual Trigger – For testing purposes (can be replaced with Cron or Webhook) Scrapeless Google Search – Queries target B2B topics via Scrapeless’s Deep SERP API Item Lists – Splits search results into individual items Scrapeless Crawler – Visits each company domain and scrapes structured content Code Node (Data Cleaner) – Extracts and formats content for LLM input Claude Sonnet (via HTTP Request) – Evaluates lead quality, relevance, and contact info Code Node (Parser) – Parses Claude’s JSON response IF Filter – Filters leads based on score threshold Discord Webhook – Sends formatted message with company info 🧩 Customization Guidance You can easily adjust the workflow to match your needs: Lead Criteria**: Modify the Claude prompt and scoring logic in the Code node Output Channels**: Replace the Discord webhook with Slack, Email, Airtable, or any CRM node Search Topics**: Change your query in the Scrapeless SERP node to find leads in different niches or countries Scoring Threshold**: Adjust the filter logic (lead_score >= 6) to match your quality tolerance 🧪 How to Use Insert your Scrapeless and Claude API credentials in the designated nodes Replace or configure the Discord webhook (or alternative outputs) Run the workflow manually (or schedule it) View qualified leads directly in your chosen notification channel 📦 Output Example Each qualified lead includes: 🏢 Company Name 🌐 Website ✉️ Email(s) 📞 Phone(s) 📍 Location 📈 Lead Score 📝 Summary of relevant content 👥 Ideal Users This workflow is perfect for: AI SaaS companies** targeting mid-market & enterprise leads Marketing agencies** looking for B2B-qualified leads Automation consultants** building scraping solutions No-code developers** working with n8n, Make, Pipedream Sales teams** needing enriched prospecting data