Workflow Templates

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

Automating your marketing campaign management process can streamline your workflow and save you valuable time. With the combination of Baserow and n8n, you can efficiently handle your campaign data and seamlessly publish content to your Shopify store. In this workflow template, I demonstrate how to leverage Baserow as a centralized platform for organizing your marketing campaign assets, including copy and images. By utilizing n8n, we automate the process of fetching images and campaign descriptions from Baserow and uploading them directly to your Shopify store. With this automated solution, you can expedite the publishing process, ensuring that your campaigns are launched swiftly across your sales channels. Additionally, this workflow serves as a foundational step towards further automation in campaign management, allowing you to dynamically generate and upload content to your Shopify store with ease. This template will help you: Use n8n to get images for marketing campaigns from Baserow and upload them to your Shopify media library Dynamically inject data from Baserow into a template file Upload a template file to your Shopify theme This template will demonstrate the follwing concepts in n8n: use the Webhook node use the IF node to control the execution flow of the workflow do time calculation using expressions and javascript use the GraphQL node to upload images to your Shopify media files create a dynamic template file for your Shopify theme use the HTTP Reqest node to upload your template file to your Shopify store How to get started? Create a custom app in Shopify get the credentials needed to connect n8n to Shopify This is needed for the Shopify Trigger Create Shopify Acces Token API credentials n n8n for the Shopify trigger node Create Header Auth credentials: Use X-Shopify-Access-Token as the name and the Acces-Token from the Shopify App you created as the value. The Header Auth is neccessary for the GraphQL nodes. You will need a running Baserow instance for this. You can also sign up for a free account at https://baserow.io/ Please make sure to read the notes in the template. For a detailed explanation please check the corresponding video: https://youtu.be/Ky-dYlljGiY

Welcome to my AI Social Media Caption Creator Workflow! What this workflow does This workflow automatically creates a social media post caption in an editorial plan in Airtable. It also uses background information on the target group, tonality, etc. stored in Airtable. This workflow has the following sequence: Airtable trigger (scan for new records every minute) Wait 1 Minute so the Airtable record creator has time to write the Briefing field retrieval of Airtable record data AI Agent to write a caption for a social media post. The agent is instructed to use background information stored in Airtable (such as target group, tonality, etc.) to create the post. Format the output and assign it to the correct field in Airtable. Post the caption into Airtable record. Requirements Airtable Database: Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) Example of an editorial plan in Airtable: Editorial Plan example in Airtable For this workflow you need the Airtable fields "created_at", "Briefing" and "SoMe_Text_AI" Feel free to contact me via LinkedIn, if you have any questions!

🧵 Generate Conversational Twitter/X Threads with GPT-4o AI (n8n Workflow) This workflow uses OpenAI (GPT-4o) and Twitter/X to automatically generate and publish engaging, conversational threads in response to a trigger (e.g., from a chatbot or form). 🚀 What Does It Do? Listens for an incoming message (e.g., via webhook or another n8n input). Uses GPT-4o to craft a narrative-style Twitter thread in a personal, friendly tone. Publishes the first tweet, then automatically posts each following tweet as a reply—building a full thread. 🛠️ What Do You Need to Configure? Before using this template, make sure to set up the following credentials: OpenAI Add your OpenAI API key in the OpenAI Chat Model node. This is used to generate the thread content. Twitter/X Add your Twitter OAuth2 credentials to the First Tweet and Thread Reply nodes. This allows the workflow to publish tweets on your behalf. ✨ Who Is This For? This template is perfect for: Content creators who want to share ideas regularly Personal brands looking to grow their presence Social media managers automating thread creation 🔧 How to Customize It You can easily adjust the tone, structure, or length of the threads by modifying the system prompt in the OpenAI node. For example: To create threads with humor, change the prompt to “Write in a witty and humorous tone.” To tailor it for marketing, prompt it with “Write a persuasive product-focused Twitter thread.” You can also integrate this workflow with: Telegram bots Web forms (e.g., Typeform, Tally) CRM tools or newsletter platforms 📋 Sample Output Prompt sent to the workflow: “Tips for growing on Twitter in 2025” Generated thread: ++Tweet 1:++ Thinking of growing your presence on Twitter/X in 2024? Here's a thread with the most effective strategies that actually work 🧵 ++Reply 1:++ Engage, don’t broadcast Twitter is a conversation platform. Reply to others, quote-tweet, and start discussions instead of just posting links. ++Reply 2:++ Consistency beats virality Tweeting regularly builds trust and visibility. You don't need to go viral — just show up.

Disclaimer This workflow contains a community node. What It Does Leverage the power of GPT-4o to seamlessly summarize a scientific research PDF of your choosing. By simply downloading a PDF of a scientific research article into a folder on your computer this powerful workflow will automatically read the article and produce a detailed summarization of the article. The workflow will then save this summarization onto your computer for future convenience. Who Is This For? The workflow is the perfect tool for all types of self-learners attempting to improve their knowledge base as efficiently as possible. It is a way to rapidly improve your knowledge base using peer reviewed scientific articles in a quick and efficient way. This workflow will provide a more detailed summary of the scientific research article than a typical abstract, while taking a fraction of the time it would take to read an entire paper. It will provide you with enough information to have a firm grasp on the information provided within the scientific article and will allow you to determine if you would like to dive deeper into the article. This workflow is perfect for professionals who need to stay current on the most recent literature in their field, as well as the self-learners who enjoy diving deep into a specific topic. It can aid anyone who is performing academic research, a literature review, or attempting to increase their knowledge base in a field using peer reviewed sources. How It Works Utilizing the power of GPT-4o, the moment you save a PDF of a scientific research article to a predesignated folder it will being to read the article and produce a summary that will be saved into another designated folder on your computer via the following steps below. Search the internet and your favorite journal databases for a scientific article that interests you. With the n8n workflow activated, download a PDF of the scientific article and save it to a specific designated folder. Saving the scientific article to this folder will trigger the workflow to initiate. The workflow will then extract the contents of the PDF and pass the data along to an AI agent utilizing the power of GPT-4o. This AI agent will produce a detailed summary of the scientific article. This summary will include the following: Introduction heading discussing the importance of the article and the specific aims of the study Methods heading detailing how the study was conducted, what variables they evaluated, what their inclusion and exclusion criteria were, and what their measurement standards were. Results heading providing specific data provided in the study for all variables tested as well as the statistical significance of each result. Summary heading evaluating the importance of the results, how it compares to other scientific articles in the same field, as well as the recommendations of the authors on how to interpret the data provided by the results. Conclusion heading summarizing the strengths and weaknesses of the scientific article as well as providing deficiencies in knowledge on the subject that would be a good topic for future studies. After the AI agent has completed its summary, it will convert the summary to text and save it to a designated folder on your computer for future viewing. Set Up Steps You will need to create a folder on your computer where you would like to save your scientific article PDFs. You will then copy the pathway to this folder into the local file trigger node. You will need to obtain an Open AI API key from platform.openai.com/api-keys After you obtain this Open AI API key you will need to connect it to the Open AI Chat Model connected to the Summarizer Tools Agent. You will now need to fund your Open AI account. GPT-4o costs ~$0.01 to run the workflow. Finally, create a folder on your computer you wish to have the summarizations saved to. Copy the pathway to this folder into the Save to Folder node. Customization This workflow is easy to customize to a specific area of research to provide the best possible summarization. If you have a specific expertise in a field of study, you can customize the output to provide data at a higher level of understanding for that field. For example, if you are a marine biologist, you can change the portion of the text prompt in the summarizer tool from "You are a research expert who is providing data to another researcher." to "You are a marine biologist expert who is providing data to another marine biologist." Disclaimer If the pdf is too large, open AI will not be able to summarize it and will provide the error that you have reached your limit of requests.

This workflow uses a number of technologies to track the value of ETFs, stocks and other exchange-traded products: Baserow: To keep track of our investments n8n’s Cron node: To trigger the workflow compiling our daily morning briefing Webscraping: The HTTP Request & HTML Extract nodes to fetch up-to-date prices from the relevant stock exchange and structure this infromation Javascript: We’ll use the Function node to build a custom HTML body with all the relevant information Sendgrid: The Email Service Provider in this workflow to send out our email Thanks to n8n, the steps in this workflow can easily be changed. Not a Sendgrid user? Simply remove the Sendgrid node and add a Gmail node instead. The stock exchange has a REST API? Just throw away the HTML Extract node. Here’s how it works: Data Source In this scenario, our data source is Baserow. In our table, we’ll track all information needed to identify each investment product: We have two text type columns (Name and ISIN) as well as two number type columns (Count and Purchase Price). Workflow Nodes 1. Cron The Cron node will trigger our workflow to run each work day in the morning hours. 2. Baserow The Baserow node will fetch our investments from the database table shown above. 3. HTTP Request Using the HTTP Request node we can fetch live data from the stock exchange of our choice based on the ISIN. This example uses Tradegate, which is used by many German fintechs. The basic approach should also work for other exchanges, as long as they provide the required data to the public. 4. HTML Extract Since our HTTP Request node fetches full websites, we’re using the HTML Extract node to extract the information we’re looking for from each website. If an exchange other than Tradegate is used, the selectors used in this node will most likely need to be updated. 5. + 6. Set The Set nodes helps with setting the exact columns we’ll use in our table. In this case we’re first formatting the results from our exchange, then calculate the changes based on the purchase price. 7. Function Here were using a bit of Javascript magic to build an HTML email. This is where any changes to the email content would have to be made. 8. Sendgrid Finally we send out the email built in the previous step. This is where you can configure sender and recipients. Result The basic email generated by this workflow will look like so:

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 What this workflow does Defines** an array of PDF URLs. Splits** the array to process each URL individually. Downloads** each PDF using an HTTP Request. 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 A free CustomJS account. An API Key saved in n8n as credentials of type CustomJS account. Notice Community nodes can only be installed on self-hosted instances of n8n. 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 paster your API key generated from CustomJS here. Design workflow A Manual Trigger for starting workflow. A code node that returns URLs of PDF files as an array Split Out node for concurrent processing HTTP node for downloading PDF file locally 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.

Creativeathive Lemaar Door Urban AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the creativeathive/lemaar-door-urban 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: creativeathive/lemaar-door-urban API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

Creativeathive Lemaar Door Mockedup AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the creativeathive/lemaar-door-mockedup 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: creativeathive/lemaar-door-mockedup API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

This workflow demonstrates how to automate live information gathering, fact-checking, and trend analysis in response to any chat message - using a powerful AI agent, memory, and a real-time search tool. Use cases are many: This is perfect for researchers needing instant, up-to-date data; support teams providing live, accurate answers; content creators looking to verify facts or find hot topics; and analysts automating regular reports with the freshest information. How It Works The workflow is triggered whenever a chat message is received (e.g., a user question, research prompt, or data request). The message is sent to the AI Agent, which follows the following steps: First, it queries SerpAPI – Research to gather the latest real-time information and data from the web. Next, it checks the Window Buffer Memory for any related past interactions or contextual information that may be useful. Finally, it sends all collected data and context to the Google Gemini Chat Model, which analyzes the information and generates a comprehensive, intelligent response. Then, the AI Agent delivers the analyzed, up-to-date answer directly in the chat, combining live data, context, and expert analysis. How To Set Up Download and import the workflow into your n8n workspace. Set up API credentials and tool access for the AI Agent: Google Gemini (for chat-based intelligence) → connected to Node Google Gemini Chat Model. SerpAPI (for real-time web and search results) → connected to Node SerpAPI - Research. Window Buffer Memory (for richer, context-aware conversations) → connected to Node Window Buffer Memory. Open the chat in n8n and type the topic or trend you want to research. Send the message and wait for the process to complete. Receive the AI-powered research reply in the chat box. Requirements An n8n instance (self-hosted or cloud). SerpAPI** credentials for live web search and data gathering. Window Buffer Memory** configured to provide relevant conversation context in history. Google Gemini API** access to analyze collected data and generate responses. How To Customize Choose your preferred AI model: Replace **Google Gemini with OpenAI ChatGPT, or any other chat model as preferred. Add or change memory: Replace **Window Buffer Memory with more advanced memory options for deeper recall. Connect your preferred chat platform**: Easily swap out the default chat integration for Telegram, Slack, or any other compatible messaging platform to trigger and interact with the workflow. Need Help? If you’d like this workflow customized, or if you’re looking to build a tailored AI Agent for your own business - please feel free to reach out to Agent Circle. We’re always here to support and help you to bring automation ideas to life. Join our community on different platforms for assistance, inspiration and tips from others. Website: https://www.agentcircle.ai/ Etsy: https://www.etsy.com/shop/AgentCircle Gumroad: http://agentcircle.gumroad.com/ Discord Global: https://discord.gg/d8SkCzKwnP FB Page Global: https://www.facebook.com/agentcircle/ FB Group Global: https://www.facebook.com/groups/aiagentcircle/ X: https://x.com/agent_circle YouTube: https://www.youtube.com/@agentcircle LinkedIn: https://www.linkedin.com/company/agentcircle

This n8n workflow shows how to convert PDF files into PNG format with the PDF Toolkit from www.customjs.space. @custom-js/n8n-nodes-pdf-toolkit Notice Community nodes can only be installed on self-hosted instances of n8n. What this workflow does Generate** PDF file from the requested HTML. Convert** the PDF to PNG images. Use** a Code node to handle URLs that point to PDF files. Convert** the PDF to PNG format. Requirements Self-hosted** n8n instance. CustomJS API key** for converting PDF to PNG. HTML** Data to convert PDF files. Code node** for handling URL that indicates PDF file. Workflow Steps: Manual Trigger: Runs with user interaction. HTML to PDF: Request HTML Data. Convert HTML to PDF. Request PDF from Code. Convert PDF to PNG: Convert the generated PNG from PDF Usage Get API key from customJS Sign up to customJS platform. Navigate to your profile page Press "Show" button to get API key Set Credentials for CustomJS API on n8n Copy and paste your API key generated from CustomJS here. Design workflow A Manual Trigger for starting workflow. HTTP Request Nodes for downloading PDF files. Code node for handling URL that indicates PDF file. Convert PDF to PNG. 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.

Jfirma1 Test_model AI Generator Description test model Overview This n8n workflow integrates with the Replicate API to use the jfirma1/test_model 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: jfirma1/test_model API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters