Category: n8n Workflows

What This Workflow Does

This n8n workflow automates media processing and dubbing operations using the Rask API. It handles the complete lifecycle of media transformation by polling both media entities and translation projects until they reach completion, then retrieves the final dubbed content and associated assets.

How It Works

The workflow begins with an execution trigger that initiates the process. It sends HTTP requests to the Rask API to process media files and create translation projects. The workflow implements intelligent polling mechanisms that continuously check the status of both media and project processing. A switch node evaluates the current state of operations, determining whether to continue waiting or proceed to the next step. Once all processing completes successfully, the workflow retrieves and delivers the final dubbing results and associated artwork.

Use Cases

• Automated video dubbing for multilingual content distribution across global markets
• Batch processing of media files requiring translation and voice-over generation
• Content localization pipelines for streaming platforms and media companies
• Automated subtitle and dubbing generation for educational video libraries
• Marketing material adaptation for different regional and language-specific audiences

Nodes Used

• executeWorkflowTrigger – Initiates the workflow execution
• httpRequest – Sends API calls to Rask media and project endpoints
• switch – Routes workflow logic based on processing status conditions
• wait – Pauses execution between polling attempts to check processing progress
• stickyNote – Provides workflow documentation and notes
• stopAndError – Handles error conditions and halts execution when necessary

Prerequisites

• Active Rask API account with valid authentication credentials
• n8n instance with HTTP request capabilities enabled
• Understanding of media file formats supported by the Rask platform
• API documentation access for Rask media and project endpoints
• Source media files prepared and ready for processing

Difficulty Level

Intermediate. This workflow requires familiarity with API integration, polling mechanisms, and conditional logic. Users should understand how to configure HTTP requests, work with API responses, and implement status-checking loops. Basic knowledge of n8n’s node system and data flow is essential for modifications and troubleshooting.

This workflow template is shared under the n8n fair-code license. Free to use and modify.

What This Workflow Does

This automation creates a comprehensive weekly marketing summary deck by gathering Facebook ad account data, analyzing it with Claude AI, and generating a professional Google Slides presentation. The completed deck is automatically emailed to your specified recipient every week on schedule.

How It Works

The workflow operates through the following sequence:

• A scheduled trigger initiates the process on your chosen day and time each week
• The GoMarble MCP integration retrieves performance data from your Facebook ad account
• Claude AI analyzes the marketing metrics and generates insights and recommendations
• Google Drive is used to access and modify your Google Slides template
• The AI-generated content populates the slides with key metrics, trends, and analysis
• Gmail automatically sends the finalized presentation to your designated recipient
• HTTP requests handle any additional data processing or API communications needed

Use Cases

• Marketing teams generating weekly performance reports for stakeholder reviews and strategic planning
• Agency account managers delivering automated client reports without manual data compilation
• In-house marketing departments tracking weekly Facebook ad performance and ROI metrics
• Business owners monitoring marketing spend efficiency and campaign performance trends
• Digital marketing consultants preparing data-driven recommendations for campaign optimization

Nodes Used

• Schedule Trigger: Sets the weekly execution schedule for the automation
• N8N Agent: Coordinates the workflow logic and orchestrates node interactions
• GoMarble MCP Client Tool: Connects to Facebook ad account and retrieves campaign metrics
• Claude AI (LM Chat Anthropic): Analyzes data and generates written insights and recommendations
• Google Drive: Accesses and manages Google Slides templates and presentations
• Gmail: Sends the completed marketing summary deck to recipients
• HTTP Request: Handles additional API calls for data retrieval or processing
• Set: Maps and transforms data between nodes
• Code: Performs custom data manipulations and formatting
• Sticky Note: Provides workflow documentation and configuration reminders

Prerequisites

• Active n8n account with workflow creation access
• Facebook ad account with API access credentials configured
• Google account with Google Slides and Gmail enabled
• Anthropic API key for Claude AI integration
• GoMarble MCP credentials and account setup
• Pre-created Google Slides template for the marketing summary deck
• Recipient email address where the weekly report should be sent
• Facebook ad account name or identifier for data pulling

Difficulty Level

Intermediate. This workflow requires configuring multiple third-party integrations including Facebook Ads, Google Workspace, and Anthropic APIs. Basic familiarity with n8n workflows and credential management is recommended. Most setup involves authentication and customization rather than complex coding.

This workflow template is shared under the n8n fair-code license. Free to use and modify.

What This Workflow Does

This workflow automates the process of uploading videos, extracting thumbnails, and storing them in Google Drive. When a video file is submitted via HTTP webhook, the system automatically validates the file type, extracts a thumbnail image at the 5-second mark using FFmpeg, and saves both the metadata and thumbnail to your Google Drive folder. You receive a JSON response with complete file details for further processing.

How It Works

The workflow follows a structured automation pipeline:

• A webhook receives incoming HTTP video upload requests
• An if-condition node validates that the uploaded file is a legitimate video format
• FFmpeg automatically installs (if needed) and executes a command to extract a thumbnail image at the 5-second timestamp
• The binary thumbnail file is read and prepared for upload
• Google Drive node receives the thumbnail and stores it in your designated folder
• A response webhook returns JSON data containing the new file’s name, size, MIME type, and Google Drive link
• All operations are logged via sticky notes for workflow transparency

Use Cases

• Content Management: Automatically generate thumbnails for video libraries without manual editing or design tools
• E-Learning Platforms: Process student-submitted videos and organize course materials with auto-generated previews in cloud storage
• Social Media Automation: Batch process user-generated video uploads and create consistent thumbnail galleries for multi-platform distribution
• Video Archiving: Build searchable video archives where every uploaded file automatically gets indexed with a visual thumbnail in Google Drive
• Client Delivery Systems: Accept video submissions from clients, auto-generate previews, and deliver organized folders with thumbnail galleries

Nodes Used

• Sticky Note: Provides workflow documentation and step descriptions
• Webhook (Trigger): Accepts incoming HTTP POST requests with video file data
• If Condition: Validates file MIME type to ensure only video files proceed
• Write Binary File: Temporarily stores the uploaded video on the server for processing
• Execute Command: Runs FFmpeg command to extract thumbnail at 5-second mark
• Read Binary File: Loads the extracted thumbnail image into memory for upload
• Google Drive: Uploads the thumbnail file to a specified Google Drive folder
• Respond to Webhook: Returns HTTP response with file metadata and Google Drive details

Prerequisites

• Active n8n instance with webhook functionality enabled
• Google Drive account with API credentials configured in n8n
• Server access with sufficient disk space for temporary video storage
• FFmpeg installed on your system (workflow auto-installs if missing)
• File upload capability supporting binary/video MIME types
• Read and write permissions to target Google Drive folder

Difficulty Level

Intermediate. This workflow requires basic understanding of n8n node connections, webhook configuration, and FFmpeg command syntax. However, most technical complexity is abstracted into pre-configured nodes. Suitable for users comfortable with automation basics who want to add intelligent video processing to their systems.

This workflow template is shared under the n8n fair-code license. Free to use and modify.

What This Workflow Does

This workflow automates the process of adding professional voiceovers and subtitles to videos. It uses OpenAI’s Whisper for speech recognition, OpenAI TTS for text-to-speech generation, and FFmpeg for video processing. The entire pipeline is orchestrated through n8n, allowing you to process videos with minimal manual intervention.

How It Works

The workflow follows a multi-step process to transform your video content:

• A manual or form trigger initiates the workflow when you submit a video file
• The video is retrieved via FTP or HTTP request from your source location
• Whisper transcribes the audio from the video into text
• The transcription is processed and formatted for subtitle generation
• OpenAI TTS generates a professional voiceover from the transcript
• FFmpeg combines the original video, new audio track, and subtitle files
• The finished video is uploaded to Google Drive or your specified destination via FTP

Use Cases

• Content Creation: YouTubers and content creators can automatically add professional voiceovers to video content in multiple languages
• Video Localization: Translate and add localized voiceovers to videos for international audiences without hiring voice actors
• Accessibility: Generate accurate subtitles and alternative audio tracks to make video content accessible to deaf and hard-of-hearing viewers
• Podcast Production: Convert podcast episodes into video format with automated subtitles and text overlays for social media distribution
• Training Materials: Corporate training departments can batch process instructional videos with consistent voiceovers and professional subtitles

Nodes Used

• Manual Trigger: Initiates the workflow manually when you’re ready to process a video
• Form Trigger: Allows users to submit video files through a web form interface
• FTP: Retrieves video files from your FTP server or uploads finished videos
• HTTP Request: Fetches video files from URLs or sends data to external services
• SSH: Executes remote commands on servers for advanced file operations
• Whisper (OpenAI): Transcribes audio from video files into accurate text
• OpenAI TTS: Converts text into natural-sounding speech in various voices and languages
• FFmpeg (Code Node): Processes and combines video, audio, and subtitle files into the final output
• Set Node: Configures variables and data for workflow steps
• Merge Node: Combines data from multiple workflow branches
• Google Drive: Stores and manages processed video files in cloud storage
• Sticky Note: Provides documentation and workflow annotations

Prerequisites

• An n8n instance installed and running (self-hosted or cloud)
• OpenAI API key with access to Whisper and TTS models
• FFmpeg installed on your server or accessible via command line
• FTP server credentials if retrieving videos from FTP sources
• Google Drive API credentials for cloud storage integration (optional)
• SSH access to servers where FFmpeg processing will occur
• Video files in common formats (MP4, MOV, AVI, WebM)
• Sufficient API quotas for Whisper transcription and TTS generation

Difficulty Level

Intermediate to Advanced. This workflow requires familiarity with video processing concepts, API integrations, and command-line tools like FFmpeg. While n8n handles most orchestration visually, configuring the FFmpeg parameters and managing API connections demands technical knowledge. Beginners may need to adjust FFmpeg commands and debug API authentication issues.

This workflow template is shared under the n8n fair-code license. Free to use and modify.

What This Workflow Does

This n8n workflow automates the creation of viral content across multiple formats including videos, podcasts, and ASMR audio. It leverages OpenAI for intelligent content generation, ElevenLabs for professional text-to-speech conversion, and Fal.ai for advanced video processing. The workflow streamlines the entire content production pipeline from ideation through delivery, enabling creators to produce high-quality multimedia content at scale.

How It Works

The workflow begins with a form trigger that collects content briefs and topics from users. OpenAI processes these inputs to generate compelling scripts and content outlines optimized for viral engagement. The generated content is then split into batches for parallel processing. ElevenLabs converts the text scripts into natural-sounding audio in multiple voices and languages. Fal.ai handles video generation and enhancement, while the workflow orchestrates file conversions and storage. All generated assets are automatically organized and stored in Google Drive and Google Sheets for easy access and tracking.

Use Cases

• YouTube creators producing daily video content with scripted narration and engaging thumbnails
• Podcast producers generating multiple episodes weekly with AI-assisted scriptwriting and voice synthesis
• ASMR content creators automating audio production for relaxation and meditation videos
• Digital marketing agencies scaling content production for multiple client campaigns simultaneously
• Social media managers creating platform-specific content variations from a single source script

Nodes Used

• formTrigger: Captures user input and content briefs
• @n8n/openAi: Generates scripts, outlines, and creative content
• @n8n/lmChatOpenAi: Advanced language model interactions for content refinement
• ElevenLabs (httpRequest): Converts text to professional audio
• Fal.ai (httpRequest): Generates and processes videos
• googleDrive: Stores and organizes generated files
• googleSheets: Tracks content metadata and performance
• splitInBatches: Processes large content volumes efficiently
• @n8n/agent: Orchestrates complex workflow logic
• @n8n/outputParserStructured: Formats AI responses into usable data
• @n8n/informationExtractor: Extracts key data from generated content
• @n8n/toolThink: Enhances reasoning for content decisions
• convertToFile: Transforms text and data into file formats
• merge: Combines multiple data streams
• code: Custom JavaScript for advanced processing
• aggregate: Consolidates batch results
• wait: Manages API rate limits and timing
• set: Defines variables and workflow state
• manualTrigger: Allows on-demand workflow execution
• stickyNote: Documentation within the workflow

Prerequisites

• Active n8n instance or n8n Cloud account
• OpenAI API key with GPT-4 access for advanced content generation
• ElevenLabs API credentials for text-to-speech conversion
• Fal.ai API key for video generation capabilities
• Google Drive and Google Sheets connected with appropriate permissions
• Understanding of workflow automation and content creation best practices
• Access to create and manage API connections in n8n

Difficulty Level

Advanced. This workflow integrates multiple external AI services and requires familiarity with API authentication, workflow orchestration, and content production processes. Users should have experience with n8n’s node configuration and be comfortable managing API credentials. However, the template can be customized by users at all skill levels by adjusting parameters and removing unused nodes.

This workflow template is shared under the n8n fair-code license. Free to use and modify.

What This Workflow Does

This n8n workflow automates the generation of viral-worthy YouTube titles and thumbnails by analyzing top-performing video content. It takes a raw content idea, researches trending videos on YouTube using keyword analysis, evaluates their performance metrics, and uses artificial intelligence to generate optimized titles and thumbnail suggestions based on proven patterns from successful videos.

How It Works

The workflow follows a systematic process to generate data-driven content recommendations:

• You input your raw content idea into the Edit Fields node in simple format (for example: “Boil Eggs Perfectly”)
• The LLM (Language Learning Model) processes your idea and generates 3 targeted keyword variations
• Apify scraper automatically searches YouTube for videos matching these keywords and collects search results
• The workflow waits for the Apify dataset to complete its data collection
• Retrieved dataset is analyzed to calculate approximate Click-Through Rate (CTR) estimations
• Top-performing videos are filtered based on performance metrics
• The LLM analyzes patterns found in successful videos including title structure, length, language, and thumbnail characteristics
• Generated suggestions for viral titles and thumbnail concepts are produced based on identified patterns
• Results are formatted and presented in an organized HTML output

Use Cases

• YouTube content creators seeking to optimize titles and thumbnails for maximum click-through rates and viewer engagement
• Digital marketing agencies developing content strategies for multiple YouTube channels and need rapid A/B testing suggestions
• Educational content creators wanting to make their videos more discoverable through data-backed title optimization
• Product reviewers and tech YouTubers analyzing competitor content to identify trending formats and presentation styles
• Small business owners managing YouTube channels who lack expertise in viral content optimization techniques

Nodes Used

• manualTrigger: Initiates the workflow execution
• wait: Pauses execution until Apify dataset completion
• @n8n/outputParserStructured: Parses and structures LLM output into organized data
• @n8n/chainLlm: Chains multiple LLM operations together for sequential processing
• httpRequest: Makes HTTP calls to external APIs including Apify
• @n8n/lmChatMistralCloud: Integrates Mistral Cloud LLM for analysis tasks
• if: Implements conditional logic based on workflow data
• code: Executes custom JavaScript code for data processing
• merge: Combines data from multiple workflow branches
• stickyNote: Provides workflow documentation and notes
• set: Sets and maps data values throughout the workflow
• html: Formats output as HTML for presentation
• splitInBatches: Divides large datasets into manageable batches for processing
• @n8n/openAi: Integrates OpenAI LLM for title and pattern analysis
• extractFromFile: Extracts data from files
• filter: Filters data based on specified criteria

Prerequisites

• Active n8n instance or n8n cloud account with workflow creation capabilities
• Apify account with API credentials for YouTube scraping functionality
• OpenAI API key for accessing GPT models for content analysis
• Mistral Cloud API key for alternative LLM processing options
• Basic understanding of YouTube content and SEO best practices
• Raw content ideas formatted as simple descriptive phrases

Difficulty Level

Advanced. This workflow requires familiarity with API integrations, LLM prompt engineering, data parsing, and conditional logic. Users should have experience with external service authentication and data transformation concepts. Intermediate n8n users can implement this workflow with documentation reference, while beginners may require additional learning on API integration fundamentals.

This workflow template is shared under the n8n fair-code license. Free to use and modify.

What This Workflow Does

This workflow automates vehicle inspection and maintenance management by capturing inspection data through JotForm, analyzing vehicle conditions with AI, retrieving maintenance history, and generating intelligent recommendations. It streamlines the entire inspection process from data collection to actionable insights.

How It Works

The workflow begins when a vehicle inspection form is submitted through JotForm, including photos and inspection details. The data is then normalized and parsed into a structured format. The system retrieves the vehicle’s maintenance history from Google Sheets, calculates upcoming due dates, and uses OpenAI to analyze the vehicle’s condition and compliance status. Finally, AI-generated recommendations are merged with the inspection data and can be sent via email or stored for reference.

Use Cases

• Fleet management companies automating vehicle inspection reports and compliance tracking
• Auto repair shops capturing pre-service inspections and generating maintenance recommendations
• Rental car agencies documenting vehicle condition at pickup and return
• Dealerships automating pre-sale vehicle inspections and condition assessments
• Transportation logistics companies tracking vehicle maintenance schedules and safety compliance

Nodes Used

• JotForm Trigger: Captures form submissions with inspection data and photos
• Code: Processes and transforms form data into structured format
• Set: Stores variables and inspection parameters
• If: Routes workflow based on conditions and inspection results
• LM Chat OpenAI: Analyzes vehicle condition and generates AI-powered recommendations
• Agent: Coordinates multi-step operations and decision-making
• Google Sheets: Retrieves and updates vehicle maintenance history
• Gmail: Sends inspection reports and notifications
• Sticky Note: Documents workflow notes and reminders

Prerequisites

• Active n8n instance or n8n Cloud account
• JotForm account with inspection form configured
• OpenAI API key for AI analysis capabilities
• Google Sheets with vehicle maintenance records database
• Gmail account for sending inspection reports
• Vehicles or assets to inspect with detailed information

Difficulty Level

Intermediate. This workflow requires basic familiarity with form integrations, Google Sheets data management, and API connections. Users should understand conditional logic and have experience with AI prompt configuration. No advanced coding is required, but understanding data mapping and API authentication is helpful.

This workflow template is shared under the n8n fair-code license. Free to use and modify.

What This Workflow Does

This GPT-4o-powered vendor intelligence and contract management platform automates the collection, analysis, and evaluation of vendor data. It eliminates manual data gathering by reducing what typically takes hours of work into just minutes, while ensuring consistent vendor evaluation criteria across your entire procurement process.

How It Works

The workflow operates on a scheduled trigger that automatically initiates vendor intelligence gathering at your specified intervals. It retrieves vendor data through multiple HTTP requests and integrates with Airtable to store and organize information. The AI layer uses GPT-4o through an LM Chat node to analyze vendor performance data according to your custom criteria. The agent framework processes tools including code-based analysis, Google Sheets integration for spreadsheet management, and Gmail for automated communications. Conditional logic determines which actions to take based on analysis results, with final outputs stored back in Airtable and Google Sheets for team access.

Use Cases

• Automate weekly vendor performance reviews by collecting KPIs and generating AI-powered analysis reports
• Generate compliance reports for procurement teams that verify vendor certifications and contract terms
• Monitor vendor pricing changes across multiple suppliers and alert procurement teams to anomalies
• Conduct automated due diligence on new vendors by gathering and analyzing public data
• Create vendor scorecards that track quality metrics, delivery performance, and cost competitiveness over time

Nodes Used

• Schedule Trigger – Initiates workflow at specified intervals
• HTTP Request – Retrieves vendor data from external sources
• Airtable – Stores and manages vendor information and analysis results
• Merge – Combines data from multiple sources
• LM Chat OpenAI – Processes vendor data with GPT-4o intelligence
• Output Parser Structured – Formats AI analysis into structured data
• HTTP Request Tool – Additional data retrieval capabilities
• Tool Code – Custom code-based data processing
• Gmail Tool – Sends notifications and reports via email
• Google Sheets Tool – Updates spreadsheets with analysis results
• Airtable Tool – Extended Airtable operations
• Agent – Coordinates multiple tools and decision-making
• If – Conditional logic for routing workflow paths
• Gmail – Email integration for communications
• Google Sheets – Spreadsheet management and reporting
• Sticky Note – Workflow documentation and notes

Prerequisites

• OpenAI API key for GPT-4o access
• Airtable account and base configured for vendor data storage
• Google Sheets account for reporting and data management
• Gmail account for automated communications
• Access credentials for vendor data sources
• n8n account with workflow execution capability

Difficulty Level

Advanced. This workflow requires knowledge of API integration, AI prompt engineering, conditional logic, and familiarity with multiple third-party platforms. Users should be comfortable configuring authentication tokens, customizing LLM prompts, and modifying node parameters for their specific vendor data sources.

This workflow template is shared under the n8n fair-code license. Free to use and modify.

What This Workflow Does

This intelligent workflow automates vendor contract renewal management by monitoring expiration dates, sending timely reminders to your finance team via Slack, and initiating renewal communications with vendors through email. The workflow includes built-in approval gates and maintains comprehensive logs of all renewal activities in Google Sheets.

How It Works

The workflow executes daily at 6 AM and follows this process:

• Schedule trigger initiates the workflow at the specified time each morning
• Code node retrieves and processes vendor contract data
• GPT-4 mini analyzes contracts to identify those requiring renewal within a specified timeframe
• Structured output parser formats the AI analysis into actionable data
• Conditional logic determines which vendors need immediate attention
• Slack notifications alert your finance team about upcoming renewals
• Email messages are automatically sent to vendors with renewal information
• Google Sheets logs all actions for audit trails and record-keeping

Use Cases

• Software license renewals – Automatically track SaaS subscriptions and initiate renewal before expiration
• Service provider contracts – Monitor maintenance, consulting, and support agreements with vendors
• Insurance policy renewals – Ensure business insurance policies are renewed on time without lapses
• Supply chain agreements – Track supplier contracts to maintain continuity in procurement
• Maintenance contracts – Monitor equipment warranty and maintenance agreements to prevent service interruptions

Nodes Used

• Schedule Trigger – Initiates workflow execution daily at 6 AM
• Code Node – Processes and transforms contract data
• OpenAI Chat (GPT-4 mini) – Analyzes contracts and generates renewal recommendations
• Structured Output Parser – Converts AI responses into structured JSON format
• Slack – Sends notifications to finance team members
• Chain LLM – Orchestrates multi-step AI interactions
• Google Sheets – Logs renewal activity and maintains contract database
• If Condition – Routes workflow based on renewal urgency
• Email Send – Delivers renewal communications to vendors
• Sticky Note – Provides workflow documentation and notes

Prerequisites

• Active n8n instance with workflow automation enabled
• OpenAI API key for GPT-4 mini access
• Slack workspace with appropriate permissions and webhook integration
• Google Sheets account with vendor contract spreadsheet
• Email service configured for outbound communications
• Vendor contract data organized with expiration dates and renewal terms
• Pre-configured email templates for renewal communications

Difficulty Level

Intermediate. This workflow requires familiarity with API integrations, conditional logic, and data parsing. Users should have experience connecting multiple services and understanding JSON data structures. Prior exposure to GPT-based workflows is recommended but not required.

This workflow template is shared under the n8n fair-code license. Free to use and modify.

What This Workflow Does

This n8n workflow automates the vendor due diligence research process by combining Google Gemini AI and Jina AI to gather, analyze, and organize vendor information. It streamlines vendor background checks, risk assessment, and link review into a single automated pipeline that feeds results directly into Google Sheets for centralized tracking and analysis.

How It Works

The workflow operates through a multi-stage process that begins with a form submission for vendor intake. Here’s the flow:

• An intake form captures initial vendor information and triggers the automation
• Gemini AI processes and classifies vendor background data
• Jina AI extracts and analyzes web content for vendor-related links and references
• The information extractor node consolidates findings into structured data
• An intelligent agent reviews and validates the collected information
• Results are aggregated and automatically populated into Google Sheets
• Conditional logic determines next steps based on risk assessment outcomes

Use Cases

• Procurement teams automating vendor qualification and screening processes before onboarding
• Risk management departments conducting rapid due diligence on potential business partners
• Enterprise compliance teams gathering vendor background information for regulatory requirements
• Supply chain managers identifying and categorizing vendor risk levels across portfolios
• Financial institutions performing automated KYC (Know Your Customer) research on vendor entities

Nodes Used

• Form Trigger: Initiates workflow with vendor intake form submission
• Google Gemini LM Chat: Processes and analyzes vendor information using AI
• Jina AI: Extracts and analyzes web content for vendor research
• HTTP Request: Retrieves data from external sources
• Information Extractor: Structures unorganized data into standardized formats
• Agent: Orchestrates multi-step research tasks with intelligent decision-making
• If: Routes workflow based on risk assessment conditions
• Aggregate: Combines data from multiple research steps
• Google Sheets: Stores and organizes vendor research results
• Set: Defines and manages workflow variables
• Code: Executes custom logic for data transformation
• Output Parser Structured: Formats AI responses into usable data structures
• Wait: Manages timing between workflow steps
• Sticky Note: Documents workflow logic and steps

Prerequisites

• Active n8n account with workflow creation capabilities
• Google Gemini API credentials and access to Gemini AI models
• Jina AI API key for web content extraction and analysis
• Google Sheets API authentication with read and write permissions
• Existing Google Sheet prepared to receive vendor research data
• Basic understanding of n8n workflow structure and node configuration
• HTTP request configuration knowledge for external API calls

Difficulty Level

Intermediate. This workflow requires configuration of multiple AI APIs and Google authentication, along with understanding of data structure mapping and conditional logic. While the template provides the base structure, customization of vendor research criteria and risk assessment rules requires moderate technical knowledge.

This workflow template is shared under the n8n fair-code license. Free to use and modify.