Workflow Templates

This workflow automatically fetches PDF invoices from a Nextcloud folder (/Invoice/Incoming), sends them via email to a fixed recipient (invoice@example.com), sends a Telegram notification, and archives the file to /Invoice/2025/archive. Key Steps: Triggered daily at 8 AM Lists files in /Invoice/Incoming Filters for existing entries Downloads the file Sends the invoice via email Sends a Telegram message with filename Moves the file to archive 📦 Technologies used: Nextcloud SMTP Email Telegram Bot ⚙️ Use case: Perfect for freelancers or small businesses to automate recurring invoice sending with minimal effort.

This n8n template demonstrates how to automate the creation of high-quality visual content using AI. The workflow takes simple titles from a Google Sheets spreadsheet, generates detailed artistic prompts using AI, creates photorealistic images, and manages the entire process from data input to final delivery. Use cases are many: Perfect for digital marketers, content creators, social media managers, e-commerce businesses, advertising agencies, and anyone needing consistent, high-quality visual content for marketing campaigns, social media posts, or brand materials! Good to know The Gemini 2.0 Flash Exp image generation model used in this workflow may have geo-restrictions. The workflow processes one image at a time to ensure quality and avoid rate limiting. Each generated image maintains high consistency with the source prompt and shows minimal AI artifacts. How it works Automated Trigger: A schedule trigger runs every minute to check for new entries in your Google Sheets spreadsheet. Data Retrieval: The workflow fetches rows from your Google Sheets document, specifically looking for entries with "pending" status. AI Prompt Generation: Using Google Gemini, the workflow takes simple titles and transforms them into detailed, artistic prompts for image generation. The AI considers: Specific visual elements, styles, and compositions Natural poses, interactions, and environmental context Lighting conditions and mood settings Brand consistency and visual appeal Proper aspect ratios for different platforms Text Processing: A code node ensures proper JSON formatting by escaping newlines and maintaining clean text structure. Image Generation: Gemini's advanced image generation model creates photorealistic images based on the detailed prompts, ensuring high-quality, consistent results. File Management: Generated images are automatically uploaded to a designated folder in Google Drive with organized naming conventions. Public Sharing: Images are made publicly accessible with read permissions, enabling easy sharing and embedding. Database Update: The workflow completes by updating the Google Sheets with the generated image URL and changing the status from "pending" to "posted", creating a complete audit trail. How to use Setup: Ensure you have the required Google Sheets document with columns for ID, prompt, status, and imageUrl. Configuration: Update the Google Sheets document ID and folder IDs in the respective nodes to match your setup. Activation: The workflow is currently inactive - activate it in n8n to start processing. Data Input: Simply add new rows to your Google Sheets with titles and set status to "pending" - the workflow will automatically process them. Monitoring: Check the Google Sheets for updated status and image URLs to track progress. Requirements Google Gemini API** account for LLM and image generation capabilities Google Drive** for file storage and management Google Sheets** for data input and tracking n8n instance** with proper credentials configured Customizing this workflow Content Variations: Try different visual styles, seasonal themes, or trending designs by modifying the AI prompt in the LangChain agent. Output Formats: Adjust the aspect ratio or image specifications for different platforms (Instagram, Pinterest, TikTok, Facebook ads, etc.). Integration Options: Replace the schedule trigger with webhooks for real-time processing, or add notification nodes for status updates. Batch Processing: Modify the limit node to process multiple items simultaneously, though be mindful of API rate limits. Quality Control: Add additional validation nodes to ensure generated images meet quality standards before uploading. Analytics: Integrate with analytics platforms to track image performance and engagement metrics. This workflow provides a complete solution for automated visual content creation, perfect for businesses and creators looking to scale their visual content production while maintaining high quality and consistency across all marketing materials.

Preventing Google Sheets Quota Errors during Batch Processing This template provides a robust solution for dealing with Google Sheets API rate limits. It is designed for workflows that update a large number of rows in a Google Sheet and frequently fail with "too many requests" errors. The template uses a Wait node connected to the error output of the Google Sheets node, creating a retry loop that delays execution for a set period before attempting the update again. To use this template, simply replace the placeholder Google Sheets nodes with your own credentials and sheet. You can find an example Google Sheet for this template here. For a full explanation of this approach, check out the blog post here.

Create a Telegram bot that combines advanced AI functionalities with LangChain nodes and new tools. Nodes as tools and the HTTP request tool are a new n8n feature that extend custom workflow tool and simplify your setup. We used the workflow tool in the previous Telegram template to call the Dalle-3 model. In the new version, we've achieved similar results using the HTTP Request tool and the Telegram node tool instead. The main difference is that Telegram bot becomes more flexible. The LangChain Agent node can decide which tool to use and when. In the previous version, all steps inside the custom workflow tool were executed sequentially. ⚠️ Note that you'd need to select the Tools Agent to work with new tools. Before launching the template, make sure to set up your OpenAI and Telegram credentials. Here’s how the new Telegram bot works: Telegram Trigger listens for new messages in a specified Telegram chat. This node activates the rest of the workflow after receiving a message. AI Tool Agent receives input text, processes it using the OpenAI model and replies to a user. It addresses users by name and sends image links when an image is requested. The OpenAI GPT-4o model generates context-aware responses. You can configure the model parameters or swap this node entirely. Window buffer memory helps maintain context across conversations. It stores the last 10 interactions and ensures that the agent can access previous messages within a session. Conversations from different users are stored in different buffers. The HTTP request tool connects with OpenAI's DALL-E-3 API to generate images based on user prompts. The tool is called when the user asks for an image. Telegram node tool sends generated images back to the user in a Telegram chat. It retrieves the image from the URL returned by the DALL-E-3 model. This does not happen directly, however. The response from the HTTP request tool is first stored in the Agent’s scratchpad (think of it as a short-term memory). In the next iteration, the Agent sends the updated response to the GPT model once again. The GPT model will then create a new tool request to send the image back to the user. To pass the image URL, the tool uses the new $fromAI() expression. Send final reply node sends the final response message created by the agent back to the user on Telegram. Even though the image was already passed to the user, the Agent always stops with the final response that comes from dedicated output. ⚠️ Note, that the Agent may not adhere to the same sequence of actions in 100% of situations. For example, sometimes it could skip sending the file via the Telegram node tool and instead just send an URL in the final reply. If you have a longer series of predefined steps, it may be better to use the “old” custom workflow tool. This template is perfect as a starting point for building AI agentic workflow. Take a look at another agentic Telegram AI template that can handle both text and voice messages.

Izzaanel Betia AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the izzaanel/betia model. This powerful AI model can generate high-quality other 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 generated image. If you include the trigger_word used in the training process you are more likely to activate the trained object, style, or concept in the resulting image. Optional Parameters mask** (string, default: None): Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. seed** (integer, default: None): Random seed. Set for reproducible generation image** (string, default: None): Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. model** (string, default: dev): Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps. width** (integer, default: None): Width of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation height** (integer, default: None): Height of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation go_fast** (boolean, default: False): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 extra_lora** (string, default: None): Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars' lora_scale** (number, default: 1): Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora. megapixels** (string, default: 1): Approximate number of megapixels for generated image 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 other content Access the generated output from the final node API Reference Model: izzaanel/betia API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

Settyan Flash V2.0.1 Beta.10 AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the settyan/flash-v2.0.1-beta.10 model. This powerful AI model can generate high-quality other 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 generated image. If you include the trigger_word used in the training process you are more likely to activate the trained object, style, or concept in the resulting image. Optional Parameters mask** (string, default: None): Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. seed** (integer, default: None): Random seed. Set for reproducible generation image** (string, default: None): Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. model** (string, default: dev): Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps. width** (integer, default: None): Width of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation height** (integer, default: None): Height of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation go_fast** (boolean, default: False): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 extra_lora** (string, default: None): Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars' lora_scale** (number, default: 1): Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora. megapixels** (string, default: 1): Approximate number of megapixels for generated image 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 other content Access the generated output from the final node API Reference Model: settyan/flash-v2.0.1-beta.10 API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

Settyan Flash V2.0.0 Beta.10 AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the settyan/flash-v2.0.0-beta.10 model. This powerful AI model can generate high-quality other 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 generated image. If you include the trigger_word used in the training process you are more likely to activate the trained object, style, or concept in the resulting image. Optional Parameters mask** (string, default: None): Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. seed** (integer, default: None): Random seed. Set for reproducible generation image** (string, default: None): Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. model** (string, default: dev): Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps. width** (integer, default: None): Width of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation height** (integer, default: None): Height of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation go_fast** (boolean, default: False): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 extra_lora** (string, default: None): Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars' lora_scale** (number, default: 1): Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora. megapixels** (string, default: 1): Approximate number of megapixels for generated image 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 other content Access the generated output from the final node API Reference Model: settyan/flash-v2.0.0-beta.10 API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

Barbacoaexpert1 Ai Haircuts AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the barbacoaexpert1/ai-haircuts model. This powerful AI model can generate high-quality other 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 generated image. If you include the trigger_word used in the training process you are more likely to activate the trained object, style, or concept in the resulting image. Optional Parameters mask** (string, default: None): Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. seed** (integer, default: None): Random seed. Set for reproducible generation image** (string, default: None): Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. model** (string, default: dev): Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps. width** (integer, default: None): Width of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation height** (integer, default: None): Height of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation go_fast** (boolean, default: False): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 extra_lora** (string, default: None): Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars' lora_scale** (number, default: 1): Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora. megapixels** (string, default: 1): Approximate number of megapixels for generated image 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 other content Access the generated output from the final node API Reference Model: barbacoaexpert1/ai-haircuts API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

Monexia Nietgoed AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the monexia/nietgoed model. This powerful AI model can generate high-quality other 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 generated image. If you include the trigger_word used in the training process you are more likely to activate the trained object, style, or concept in the resulting image. Optional Parameters mask** (string, default: None): Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. seed** (integer, default: None): Random seed. Set for reproducible generation image** (string, default: None): Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. model** (string, default: dev): Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps. width** (integer, default: None): Width of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation height** (integer, default: None): Height of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation go_fast** (boolean, default: False): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 extra_lora** (string, default: None): Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars' lora_scale** (number, default: 1): Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora. megapixels** (string, default: 1): Approximate number of megapixels for generated image 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 other content Access the generated output from the final node API Reference Model: monexia/nietgoed API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

This n8n template demonstrates how to download multiple PDF files from public URLs and merge them into a single PDF using 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 Downloads** each PDF using an HTTP Request. Populates* files into an array with *Merge** node from n8n. Merges** all downloaded PDFs using the Merge PDF node from the @custom-js/n8n-nodes-pdf-toolkit. Writes** the final merged PDF to disk. Requirements Self-hosted** n8n instance CustomJS API key** for merging multiple PDF files. PDF files to be merged** to be converted into a PDF Workflow Steps: Manual Trigger: Runs with user interaction. HTTP Request Node For PDF Download: Pass urls for PDF files to merge. Merge Node For Array Population: Just populates two files into an array. Merge PDF files: Uses the CustomJS node to merge the incoming PDF files into a single PDF file. If size of PDF files exceeds 6MB, you can simply pass an array of URLs for PDF files. 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. Two HTTP Request Nodes for downloading PDF files. A Merge Node for populating files as an array. Merge PDFs node for merging files Write to Disk node for saving merged PDF file. 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. Perfect for Bundling reports or invoices. Generating document sets from external sources. Automating PDF handling without writing custom code

This flow migrates all repositories of a Gitlab group to a Gitea organization by triggering Gitea's integrated migration tool. Set up steps: Copy this workflow Create an empty Gitea-organization you want to migrate to. (The flow will skip all projects which have the same name of possibly already existing repos in the target Gitea organization.) Create an access token in your Gitea (https://gitea.example.com/user/settings/applications), set it up as a Header Auth with it's name being "Authorization" and value being "token [your-gitea-token]" and select it for the "Gitea:"-named nodes. Create a Personal access token in Gitlab (https://gitlab.com/-/user_settings/personal_access_tokens), create a Header Auth with name "PRIVATE-TOKEN" and value "[your-gitlab-token]" and select it for the "Gitlab:"-named node. Also keep the value of your Gitlab-token available for step 5. Edit the Set node right after the trigger node and set paste your personal access token in there as well as the names of the Gitlab source group and the Gitea target organization. Use the url-friendly version of their names by simply copy&pasting them from their URLs. Run the flow and enjoy the show :)

I wanted a system to monitor website content changes and notify me. So I made it using n8n. Especially my competitor blogs. I wanted to know how often they are posting new articles. (I used their sitemap.xml file) (The below workflow may vary) In the Below example, I used HackerNews for example. Explanation: First HTTP Request node crawls the webpage and grabs the website source code Then wait for x minutes Again, HTTP Node crawls the webpage If Node compares both results are equal if anything is changed. It’ll go to the false branch and notify me in telegram. Workflow: Sample Response: