Workflow Templates

Base64 Encode Multiple Binary Files with a Code Node This template demonstrates how to handle multiple binary files in n8n by using a Code node to convert them into a Base64 encoded string. It's particularly useful when an API requires file uploads in this format and the standard 'Extract From File' node is not sufficient for batch processing. The workflow starts by downloading a ZIP file, unzipping it to get multiple binary files, and then uses a Code node with custom JavaScript to encode each file individually. Instructions Download and import this template into your n8n instance. Run the workflow once to see how it downloads, unzips, and then encodes multiple files. Modify the 'HTTP Request' node to download your own binary file or a ZIP file containing multiple files. Update the 'Code' node if you need to adjust the output format or file paths. Use the output of the 'Code' node in a subsequent node, such as another 'HTTP Request' to send the Base64-encoded files to your desired API. A link to the full blog post is available here

What It Does The Chef Agent is your AI-powered kitchen companion—ready to turn leftover ingredients into meal inspiration. It's a simple, fun n8n automation that: Accepts a list of ingredients via webhook Uses Ollama AI to suggest 5 creative recipes or food ideas Recommends up to 3 missing ingredients to improve the dish Returns a fallback message if the AI is unavailable Includes setup notes for beginners Requirements An active n8n instance (local or hosted) Ollama AI running locally (or another LLM via HTTP request) A webhook endpoint (defaults to /lets-cook) Why You’ll Love It Fully customizable for your use case or favorite LLM Great intro to AI + workflow automation Comes with playful Clown Mutiny flair: > “Powered by Clown Mutiny’s taste-bud liberation division.” Installation Import the provided JSON template into your n8n workspace. Configure your AI node to match your local Ollama instance. Trigger the flow by sending a POST request to the webhook: { "ingredients": "eggs, rice, spinach" }

Let a user load multiple files with a Form node, and process the binary data. A very important workflow for many tools. This is a learning example of several core concepts that are hard to grasp in n8n: $binary data Loop and $runIndex Split Out The Save File deomonstrates how to access the binary data correctly, but could be swapped to POST the files to an AI, for example.

Wan Video Wan 2.2 I2v A14b Video Generator Description Image-to-video at 720p and 480p with Wan 2.2 A14B Overview This n8n workflow integrates with the Replicate API to use the wan-video/wan-2.2-i2v-a14b model. This powerful AI model can generate high-quality video 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): Prompt for video generation image** (string): Input image to generate video from Optional Parameters seed** (integer, default: None): Random seed. Leave blank for random num_frames** (integer, default: 81): Number of video frames. 81 frames give the best results resolution** (string, default: 480p): Resolution of video. 832x480px corresponds to 16:9 aspect ratio, and 480x832px is 9:16 sample_shift** (number, default: 5): Sample shift factor sample_steps** (integer, default: 30): Number of generation steps. Fewer steps means faster generation, at the expensive of output quality. 30 steps is sufficient for most prompts frames_per_second** (integer, default: 16): Frames per second. Note that the pricing of this model is based on the video duration at 16 fps 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 video content Access the generated output from the final node API Reference Model: wan-video/wan-2.2-i2v-a14b API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of video generation parameters

Modular Hazard Analysis Workflow : Free Version Business Value Proposition Accelerates ISO 26262 compliance for automotive/industrial systems by automating safety analysis while maintaining rigorous audit standards. :chart_with_upwards_trend: Key Benefits Time Instant report generation vs. weeks of documentation for HAZOP Risk Mitigation Pre-validated templates reduce human error Quick guide Input a systems_description file to the workflow Provide an OPENAI_API_KEY to the chat model. You can also replace the chat model with the model of your interest. :play_or_pause_button: Running the Workflow Refer to the github repo to understand in detail about how the workflow can be used :email: Contact For collaboration proposals or security issues, contact me by Email. :warning: Validation & Limitations AI-Assisted Analysis Considerations | Advantage | Mitigation Strategy | Implementation Example | |-----------|---------------------|------------------------| | Rapid hazard identification | Human validation layer | Manual review nodes in workflow | | Consistent S/E/C scoring | Rule-based validation | ASIL-D → Redundancy check | | Edge case coverage | Cross-reference with historical data | Integration with incident databases |

Who is this for? This workflow is ideal for sales teams, marketers, and virtual assistants who manage outbound campaigns and want to improve their cold outreach personalization. It helps automate the research and writing process for each lead, saving time while improving quality. What problem is this workflow solving? Cold outreach often lacks personalization because manually reviewing each lead's website takes time. This workflow eliminates that bottleneck by using AI to auto-generate personalized icebreakers, summaries, and outreach emails based on a lead’s website—without human research. What this workflow does This n8n workflow runs on a schedule and pulls leads from Airtable who don't yet have an "Ice breaker" field filled out. For each lead, it does the following: Trigger: Scheduled daily via the Run Daily to Process New Leads node. Search Airtable: Finds leads in Airtable where the Ice breaker field is empty using the Search Cold Leads Without Icebreaker node. Split in Batches: Iterates through each lead one by one using Loop Through Each Lead. Rate Limiting: Waits briefly before each request using Wait Before Making Request to avoid rate limits. Scrape Website: Sends each lead’s website to Dumpling AI's /scrape endpoint via the Scrape Lead Website with Dumpling AI HTTP request. Generate AI Copy: Sends the scraped content to GPT-4o using the Generate Icebreaker, Summary & Email (GPT-4o) node. It asks the LLM to create: A short personalized icebreaker A 2–3 line website summary A short email body for cold outreach Save Results: Updates the original Airtable record with the generated content using the Save AI Output Back to Airtable node. Sticky Note: Provides an overview of the workflow and usage instructions for future editors or collaborators. This loop continues for all leads found, updating Airtable with fresh AI-generated outreach content. Integration Requirements Airtable (Personal Access Token) Dumpling AI API Key (Header Auth) OpenAI (GPT-4o)

This n8n workflow automatically fetches the latest CVE data at scheduled intervals, extracts relevant security details, and creates a corresponding Security Incident in ServiceNow for each new vulnerability. Schedule Trigger – Runs at predefined intervals. Jina Fetch – Retrieves the latest CVE feed. Information Extractor (OpenAI Chat Model) – Processes and extracts key details from the CVE data. Split Out – Separates each CVE entry for individual processing. Create Incident – Generates a ServiceNow Security Incident with the extracted CVE details. Ideal for security teams to ensure timely tracking and remediation of new vulnerabilities without manual monitoring.

Zsxkib Canary Qwen 2.5b Text Generator Description 🎤The best open-source speech-to-text model as of Jul 2025, transcribing audio with record 5.63% WER and enabling AI tasks like summarization directly from speech✨ Overview This n8n workflow integrates with the Replicate API to use the zsxkib/canary-qwen-2.5b model. This powerful AI model can generate high-quality text 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 audio** (string): Audio file to transcribe Optional Parameters llm_prompt** (string, default: None): Optional LLM analysis prompt show_confidence** (boolean, default: False): Show AI reasoning in analysis include_timestamps** (boolean, default: True): Include timestamps in transcript 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 text content Access the generated output from the final node API Reference Model: zsxkib/canary-qwen-2.5b API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of text generation parameters

This workflow is triggered when a new deal is created in HubSpot. Then, it processes the deal based on its value and stage. The first branching follows three cases: If the deal is closed and won, a message is sent in a Slack channel, so that the whole team can celebrate the success. If a presentation has been scheduled for the deal, then a Google Slides presentation template is created. If the deal is closed and lost, the deal’s details are added to an Airtable table. From here, you can analyze the data to get insights into what and why certain deals don’t get closed. The second branching follows two cases: If the deal is for a new business and has a value above 500, a high-priority ticket assigned to an experienced team member is created in HubSpot If the deal is for an existing business and has a value below 500, a low-priority ticket is created.

Overview This workflow implements a privacy-preserving AI document processing pipeline that detects, masks, and securely manages Personally Identifiable Information (PII) before any AI processing occurs. Organizations often need to analyze documents such as invoices, forms, contracts, or reports using AI. However, sending documents containing personal data directly to AI models can create serious privacy, compliance, and security risks. This workflow solves that problem by automatically detecting sensitive information, replacing it with secure tokens, and storing the original values in a protected vault database. Only the masked version of the document is sent to the AI model for analysis. If required, a controlled PII re-injection mechanism can restore original values after processing. The workflow also records all operations in an audit log, making it suitable for environments requiring strong compliance such as GDPR, financial services, healthcare, or enterprise document processing systems. How It Works 1. Document Upload A webhook receives a document (typically a PDF) and triggers the workflow. 2. OCR Text Extraction The OCR Extract node extracts the text content from the document so it can be analyzed for sensitive information. 3. PII Detection Multiple detectors analyze the text to identify different types of sensitive data: Email addresses (regex detection) Phone numbers (multi-pattern detection) Identification numbers such as PAN, SSN, or bank accounts Physical addresses detected using an AI model Each detection includes: detected value location in the text confidence score 4. Detection Consolidation All detected PII results are merged into a single dataset. The workflow resolves overlapping detections and removes duplicates to produce a clean list of sensitive values. 5. Tokenization and Secure Vault Storage Each detected PII value is replaced with a secure token, for example: <<EMAIL_7F3A>> <<PHONE_A12B>> The original values are securely stored in a Postgres vault table. This ensures sensitive data is never exposed to AI models. 6. Masked AI Processing The masked document is sent to an AI model for structured analysis. Possible AI tasks include: Document classification Data extraction Document summarization Entity extraction Since all sensitive data has been tokenized, the AI processes the document without seeing any real personal data. 7. Controlled PII Re-Injection After AI processing, the workflow can optionally restore original values from the vault. The Re-Injection Controller determines which fields are allowed to restore PII based on defined permissions. 8. Compliance Audit Logging All events are recorded in an audit table, including: PII detection token generation AI processing PII restoration This provides traceability and compliance reporting. Setup Instructions 1. Configure Postgres Database Create two tables in your database. PII Vault Table Example structure: token original_value type document_id created_at This table securely stores original PII values mapped to tokens. Audit Log Table Example structure: document_id pii_types_detected token_count ai_access_confirmed re_injection_events timestamp actor This table records workflow activity for compliance tracking. 2. Configure AI Model Credentials This workflow supports multiple AI models: Anthropic Claude (used for AI document processing) Ollama local models (used for address detection) Configure credentials in n8n before running the workflow. 3. Configure Webhook Trigger The workflow starts when a document is sent to the webhook: POST /webhook/gdpr-document-upload Upload a PDF file to this endpoint to trigger processing. 4. Configure Alert Notifications (Optional) Replace the placeholder alert webhook URL with your monitoring or alerting system. Example use cases: Slack alert monitoring system incident notification Alerts are triggered if masking fails. Use Cases This workflow is useful for many privacy-sensitive automation scenarios. GDPR-Compliant Document Processing Safely process documents containing personal data without exposing PII to AI models. AI-Powered Document Analysis Use AI to summarize or extract data from documents while maintaining privacy. Enterprise Data Redaction Pipelines Automatically detect and tokenize sensitive data before sending documents to downstream systems. Financial Document Processing Process invoices, contracts, and financial reports securely. Healthcare Document Automation Analyze patient documents while ensuring sensitive data is protected. Requirements To run this workflow you need: n8n** Postgres database** Anthropic Claude API access** Ollama (optional for local AI address detection)** Webhook endpoint for document uploads** Optional integrations: Monitoring or alert system Compliance audit database Key Features Automated PII detection and tokenization AI-safe document processing** Secure vault storage for sensitive data Controlled PII restoration Full audit logging Works with multiple AI models Designed for GDPR and enterprise compliance Summary This workflow creates a secure bridge between sensitive documents and AI systems. By automatically detecting, masking, and securely storing personal data, it enables organizations to safely apply AI to document processing tasks without exposing sensitive information. The combination of tokenization, secure vault storage, controlled re-injection, and audit logging makes this workflow suitable for privacy-sensitive industries and enterprise automation pipelines.

Who is this template for ? Basically anyone involved in recurring recruiting processes and looking to save a considerable amount of time and energy (Talent acquisitions Managers, recruiting consultants, hiring managers, founders…etc) What it does : It takes a messy and raw transcript from an “intake meeting” between a recruiter and a Hiring manager and turns it into a clean and exhaustive brief + scorecard templates for each interview rounds It does it under 1 MINUTE while the usual “manual” process usually takes several hours How to customize this workflow to your needs Google doc is the default choice because it allows easy modification of the output, but you can choose to output this under any format and / or store it wherever you want I strongly suggest to choose one of the latest LLM models for better output quality Both LLM prompts can be revised to match your expectations better

Welcome to my Automated Image Metadata Tagging Workflow! DISCLAIMER: This workflow only works with self-hosted n8n instances! You have to install the n8n-nodes-exif-data Community Node! This workflow automatically analyzes the image content with the help of AI and writes it directly back into the image file as keywords. (https://n8n.io/workflows/2995).** This workflow has the following steps: Google Drive trigger (scan for new files added in a specific folder) Download the added image file Analyse the content of the image Merge Metadata and image file Write the Keywords into the Metadata (dc:subject/keywords) and create new image file Update the original file in the Google Drive folder The following accesses are required for the workflow: You have to install the n8n-nodes-exif-data Community Node** Google Drive: Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) You can contact me via LinkedIn, if you have any questions: https://www.linkedin.com/in/friedemann-schuetz