Workflow Templates

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

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

This n8n workflow is designed for working with the WhatsApp Business platform. It allows to send custom replies via WhatsApp in response to incoming user messages. 💡 Take a look at the step-by-step tutorial on how to create a WhatsApp bot. The workflow consists of two parts: The first Verify webhook sends back verification challenge string. You will need this part during the setup process on the Meta for Developers portal: Select your App Go to WhatsApp Configuration Click on the Edit button in the Webhook session Enter your production webhook URL, provide Verify token (can be any text string) Remember to activate the n8n workflow! Finally press "Verify and save" Once the webhook is verified, the Respond webhook receives various POST requests from Meta regarding WhatsApp messages (user messages and status notifications). The workflow checks whether the incoming JSON contains a user message. If this is the case, it sends the text message back to the user. This is a custom message, not a WhatsApp Business template.

This workflow demonstrates how to use noItemsLeft to check if there are items left to be processed by the SplitInBatches node. Function node: This node generates mock data for the workflow. Replace it with the node whose data you want to split into batches. SplitInBatches node: This node splits the data with the batch size equal to 1. Based on your use-case, set the value of the Batch Size. IF node: This node check if all the data by the SplitInBatches are not processed or not. It uses the expression {{$node["SplitInBatches"].context["noItemsLeft"]}} which returns a boolean value. If there is data yet to be processed, the expression will return false, otherwise true. Set node: This node prints a message No Items Left. Based on your use-case, connect the false output of the IF node to the input of the node you want to execute, after the data is processed by the SplitInBatches node.

Who is it for? If you are having a lot of meetings as a project manager, CFO, CTO, CEO or any other role that requires handling many meetings, AND you are working with people in different timezones, you may have noticed that it is not uncommon that daylight savings time change day may differ from timezone to timezone. This may be very troublesome at times. If DST change day differs between timezones, then you might need to adjust your meetings time accordingly. And this happens twice a year. So it's good to get notification beforehand (at least a day before). This automation will notify you if tomorrow you can expect DST in any zone you provide. How It Works? Script runs daily and loops through provided timezones Checks if there is DST change to or from the tomorrow (if you want to be notified sooner, just adjust number of days) If there is DST change, script provides you with Slack notification (replace with email if needed) How to set up? Add and/or edit timezones you want to monitor in "Timezones List" node Adjust "Calculate Tomorrow's Date" if you want to be notified sooner than 1 day before DST change Adjust "Send Notification on Upcoming Change" to set where on Slack you want to be notified And that's it. Hope that you won't miss any other meeting because of DST!

Lucataco Seed X Ppo Text Generator Description Seed-X-PPO-7B by ByteDance-Seed, a powerful series of open-source multilingual translation language models Overview This n8n workflow integrates with the Replicate API to use the lucataco/seed-x-ppo 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 text** (string): Text to translate target_language** (string): Target language (e.g., 'Chinese', 'French', 'Spanish') Optional Parameters num_beams** (integer, default: 4): Number of beams for beam search max_length** (integer, default: 512): Maximum length of generated text source_language** (string, default: auto): Source language (use 'auto' for automatic detection) 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: lucataco/seed-x-ppo API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of text generation parameters

🧠 Writes original, thought-provoking blog posts using AI 🕓 Runs every 12 hours automatically ✍️ Publishes directly to Ghost blog with title, tags, and SEO meta 🔧 Features Scheduled every 12 hours OpenAI generates a multi-part blog post with metadata Markdown-compatible output (no HTML) Automatically published to Ghost CMS using authenticated API (🔐 no hardcoded keys) Fully modular and general-purpose — edit prompt for any blog theme! ⚙️ Nodes Overview Step Node Type Purpose 1️⃣ Schedule Trigger Runs every 12 hours 2️⃣ OpenAI Generates blog post + meta info 3️⃣ Code Extracts content, title, meta, and tags 4️⃣ Code Formats content as Ghost mobiledoc payload 5️⃣ HTTP Request Publishes post to Ghost via Admin API 📝 OpenAI Prompt (Generalized) Write a high-quality blog post on a creative or thought-provoking topic. The tone should be engaging and immersive. Length: 2–4 paragraphs. Then add a brief paragraph offering an alternative perspective or logical counterpoint. Finally, generate: Blog post title Meta description 5 tags 🔐 Notes ✅ No hardcoded API keys 🛠️ Ghost Admin API credentials must be set using the Credential Manager 📌 Prompt and Ghost URL are both easily customizable

This n8n workflow was developed to evaluate and categorize incoming leads based on certain criteria. The workflow is triggered by adding a new row in a Google Sheets document. The workflow uses the OpenAI node to process the lead information. The system query contains detailed qualification rules and the response format. The user message contains the data for the individual lead. The JSON response from the OpenAI node is then processed by the Edit Fields node to extract the response. This response is merged together with the original lead data by the Merge node. Finally, the Google Sheets node updates the original lead entry in the Google Sheets document with the qualification result ("qualified" or "not qualified") in a separate column. This allows for easy tracking and sorting of the qualified leads.

This workflow performs various Git operations. It starts with a manual trigger, sets the local repository path, decodes a file and then updates a file's content, adds, commits, and pushes changes to a GitHub repository, and finally pulls changes. The upper branch of the workflow retrieves a specific file ("README.md") from a GitHub repository ("git_push_article") owned by "teds-tech-talks." It then decodes the file's binary data into readable text using a code node. The decoded content is used to update the file by adding a timestamp and data. Finally, the modified file is pushed back to the repository using a GitHub node, completing the process of editing and updating the file directly via the workflow. This bottom branch of the workflow makes changes to a local Git repository. It starts by updating the "README.md" file with a timestamp and some content. Then, it adds the modified files, commits the changes with a message, and pushes them to a remote GitHub repository owned by "teds-tech-talks." Additionally, the workflow allows pulling changes from the remote repository into the local repository. The goal is to demonstrate how to perform various Git operations using n8n nodes, including adding, committing, pushing, and pulling changes.

This workflow demonstrates how to use currentRunIndex to get the running index. Function node: This node generates mock data for the workflow. Replace it with the node whose data you want to split into batches. SplitInBatches node: This node splits the data with the batch size equal to 1. Based on your use-case, set the value of the Batch Size. IF node: This node checks the running index. If the running index equals 5 the node returns true and breaks the loop. The node uses the expression {{$node["SplitInBatches"].context["currentRunIndex"];}}, which returns the running index. Set node: This node prints a message Loop Ended. Based on your use-case, connect the false output of the IF node to the input of the node you want to execute if the condition is false.

Tags*: Supply Chain, Logistics, Route Planning, Transportation, GPS API Context Hi! I’m Samir — a Supply Chain Engineer and Data Scientist based in Paris, and founder of LogiGreen Consulting. I help companies improve their logistics operations using data, AI, and automation to reduce costs and minimize environmental footprint. > Let’s use n8n to build smarter and greener transport operations! 📬 For business inquiries, you can add find me on LinkedIn Who is this template for? This workflow is designed for logistics and transport teams who want to automate distance and travel time calculations for truck shipments. Ideal for: Control tower dashboards Transport cost simulations Route optimization studies How does it work? This n8n workflow connects to a Google Sheet where you store city-to-city shipment lanes, and uses the OpenRouteService API to calculate: 📏 Distance (in meters) ⏱️ Travel time (in seconds) 🪪 Number of route steps Steps: ✅ Load departure/destination city coordinates from a Google Sheet 🔁 Loop through each record 🚚 Query OpenRouteService using the truck (driving-hgv) profile 🧾 Extract and store results: distance, duration, number of steps 📤 Update the Google Sheet with new values What do I need to get started? This workflow is beginner-friendly and requires: A Google Sheet with route pairs (departure and destination coordinates) A free OpenRouteService API key 👉 Get one here Next Steps 🗒️ Follow the sticky notes inside the workflow to: Select your sheet Plug in your API key Launch the flow! 🎥 Check the Tutorial 🚀 You can customize the workflow to: Add CO2 emission estimates for Sustainability Reporting Connect to your TMS via API or EDI This template was built using n8n v1.93.0 Submitted: June 1, 2025