Workflow Templates

Use Case It is very convenient to add expenses via simple chat message. This workflow attempts to do exactly this using AI-powered n8n magic! Send message to a chat, something like "car wash; 59.3 usd; 25 jan 2024" And get a response: Your expense saved, here is the output of save sub-workflow:{"cost":59.3,"descr":"car wash","date":"2024-01-25","msg":"car wash; 59.3 usd; 25 jan 2024"} LLM will smartly parse your message to structured JSON and save the expense as a new row into Google Sheet! Installation 1. Set up Google Sheets: Clone this Sheet: https://docs.google.com/spreadsheets/d/1D0r3tun7LF7Ypb21CmbTKEtn76WE-kaHvBCM5NdgiPU/edit?gid=0#gid=0 (File -> Make a copy) Choose this sheet into "Save expense into Google Sheets" node. 2. Fix sub-workflow dropdown: open "Parse msg and save to Sheets" node (which is an n8n sub-workflow executor tool) and make sure the SAME workflow is chosen in the dropdown. it will allow n8n to locate and call "Workflow Input Trigger" properly when needed. 3. Activate the workflow to make chat work properly. Sent message to chat, something like "car wash; 59.3 usd; 25 jan 2024" you should get a response: Your expense saved, here is the output of save sub-workflow:{"cost":59.3,"descr":"car wash","date":"2024-01-25","msg":"car wash; 59.3 usd; 25 jan 2024"} and new row in Google sheets should be inserted!

The Sound Effects Generator is an automated workflow that allows users to create realistic sound effects using AI and save them directly to Google Drive. It generates high-quality sound effects (up to 30 seconds long) based on user prompts. How It Works: User Input via Web Form A form is presented to the user asking for: A prompt describing the sound (e.g. "waves crashing", "laser blast"). A duration in seconds (up to 30 seconds). API Request to Generate Audio The input is sent to CassetteAI via a POST request using API with proper authentication. Status Polling The workflow waits for 10 seconds and then checks the status of the request. Conditional Flow If the audio generation is complete (COMPLETED), it proceeds to fetch the audio file URL. If not, it waits and retries. Download & Save The audio file is downloaded from the URL. It is automatically uploaded to a specific folder in the user’s Google Drive, with a timestamped filename. Key Advantages Fast & Efficient**: Generates up to 30 seconds of audio in just 1 second of processing time. No Coding Required**: Entire flow can be triggered via a simple form interface. Automated Storage**: Files are automatically saved to a preconfigured Google Drive folder. Scalable**: Can be reused for multiple projects by simply changing the input prompts. Secure**: Uses secure API key-based authentication for interaction with Fal.run and Google Drive. Customizable**: Easy to adapt or extend—for example, sending download links via email or Telegram. How It Works Form Submission: The workflow starts with a form where users input a prompt and the desired duration (max 30 seconds) for the sound effect. Audio Creation: The submitted data is sent to the CassetteAI Sound Effects Generator API via an HTTP request, which initiates the sound effect generation process. Status Check: The workflow periodically checks the status of the request. If the status is "COMPLETED," it proceeds to fetch the audio file. Audio Retrieval: The generated audio file is downloaded from the provided URL and uploaded to a specified Google Drive folder, with a timestamped filename for organization. Set Up Steps API Key Configuration: Create an account on fal.ai and obtain an API key. In the "Create audio" node, set the "Header Auth" with: Name: Authorization Value: Key YOURAPIKEY (replace YOURAPIKEY with your actual API key). Google Drive Integration: Ensure the Google Drive node is configured with the correct OAuth2 credentials and folder ID. Adjust the folder ID in the "Upload Audio" node if a different destination is preferred. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Use case n8n workflows can go out of hand when you're automating as much as we do at n8n. We needed a place to document them and keep track of who owns and maintains them. To facilitate this we use this n8n workflow to automatically sync workflows directly to a Notion database if it has the tag sync-to-notion. How to setup Add your n8n api creds Add your Notion creds Create notion database with fields env id as text, isActive (dev) as boolean, URL (dev) as url, Workflow created at as date, Workflow updated at as date, Error workflow setup as boolean (Make sure page is connected) Add tag sync-to-notion to some workflows

Who is this for? For developers and organizations that need to convert DOCX files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Downloads the DOCX file from the web. Converts the DOCX file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Adjust url_to_file in the Config node to URL pointing to your file. Optionally, additional Body Parameters can be added for the converter.

This workflow allows you to check the To-Do list on Notion and send a message on Slack. Prerequisites Create a Notion page similar to this page. Create credentials for Notion by following the instructions mentioned in the documentation. Follow the steps mentioned in the documentation to create credentials for Slack. Cron node: This node triggers the workflow every day. Notion node: This node fetches all the tasks from Notion. IF node: This node checks if the task is assigned to a particular user. Create a Direct Message: This node will create a direct message channel with the user. Send a Direct Message: This node will send the to-do lists in the direct message. NoOp: This node is connected to the false output of the IF node. If the condition is false, no further action will be taken.

🎥 YouTube Video Summarizer for Social Media Turn any YouTube video into a short, structured summary using AI — perfect for content creators, marketers, or social media managers. 🔧 What We Built We created a no-code automation in n8n that: Accepts a YouTube Video ID via a form Fetches the video transcript using an external API Summarizes the transcript using AI (Google Gemini) Automatically saves the summary to Google Docs for team use 🧩 Flow Overview | Step | Description | |------|-------------| | ✅ Form Trigger | User submits a YouTube video ID using an n8n form | | 🔁 Set Node | Maps the YouTube video ID for use in the API request | | 🌐 HTTP Request (External API) | Calls the YouTube Transcriptor AI API via RapidAPI to fetch transcript | | 🧹 Formatter (Code Node) | Joins transcript lines into a readable text block | | 🧠 AI Agent + Google Gemini (via Langchain) | Summarizes the full transcript into bullet points and tone | | 🧽 Optimizer (Code Node) | Extracts just the summary from the AI response | | 📝 Google Docs Node | Appends the clean summary to a shared Google Doc | 🌍 Real-World Problem Solved ❌ The Challenge Creators and marketers waste hours watching full videos just to extract the key points. Manual summarization is inconsistent, repetitive, and delays content planning. ✅ Our Solution ⏱️ Reduces time spent watching videos 🧠 AI-powered summaries keep tone consistent and structured 📄 Auto-sync with Google Docs makes summaries instantly available for teams 🔥 Bonus: This uses the YouTube Transcriptor AI API, so no need to manually scrape captions or use browser extensions. 🚀 Ideal Use Cases Repurpose YouTube content into Instagram Reels, LinkedIn posts, or blog content Build a video summary library for your editorial team Quickly extract talking points from podcast episodes 🛠️ Tech Stack n8n** – workflow automation engine YouTube Transcriptor AI API** – via RapidAPI Google Gemini (via Langchain)** – AI summarization Google Docs** – stores the final summary JavaScript nodes** – custom text parsing & formatting 💡 Want to customize it? Add Slack, Airtable, Notion, or Tweet auto-posting to expand the flow. Create your free n8n account and set up the workflow in just a few minutes using the link below: 👉 Start Automating with n8n Save time, stay consistent, and grow your LinkedIn presence effortlessly!

This workflow integrates LINE BOT, AI Agent (GPT), Google Sheets, and Google Drive to enable users to search for file URLs using natural language. The AI Agent extracts the filename from the message, searches for the file in Google Sheets, and returns the corresponding Google Drive URL via LINE BOT. Supports natural language queries (e.g., "Find file 1.pdf for me") AI-powered filename extraction Google Sheets Lookup for file URLs Auto-response via LINE BOT How to Use This Template 1. Download & Import Copy and save the Template Code as a .json file. Go to n8n Editor → Click Import → Upload the file. 2. Update Required Fields Replace YOUR_GOOGLE_SHEET_ID with your actual Google Sheet ID. Replace YOUR_LINE_ACCESS_TOKEN with your LINE BOT Channel Access Token. 3. Activate & Test Click Execute Workflow to test manually. Set Webhook URL in LINE Developer Console. Features of This Template Supports Natural Language Queries (e.g., “Find file 1.pdf for me”) AI-powered filename extraction using OpenAI (GPT-4/3.5) Real-time file lookup in Google Sheets Automatic LINE BOT Response Fully Automated Workflow

Problem Teams often struggle with email overload, leading to missed actions and inefficient meeting preparation. Solution This workflow automates email management using n8n and AI. It fetches emails, summarizes key points and actions, and sends two concise updates—one in the morning and one at night. How It Works Triggers at 7 AM and 9 PM: Automates the process to summarize emails received during specific time blocks. Fetches Emails: Retrieves emails from the last 24 hours or after a specific time. Summarizes with AI: Uses OpenAI to process the email content into actionable summaries. Sends Team Updates: Compiles the summaries into a concise, formatted email and sends it to the team. Expected Results Significant reduction in missed actions and follow-ups. Customizations Adjust timings, filters, and recipients to suit your team’s needs.

This n8n template helps you automatically discover, analyze, and track trending topics and videos on YouTube using an AI-powered agent. Use cases are many: This workflow is perfect for YouTube creators needing fresh video ideas, digital marketers scouting new campaign topics, social media managers who want to catch trends early, and researchers who want to analyze what’s viral. How It Works The workflow starts whenever a chat message is received (e.g., a trend question, a topic prompt, or a request for insights). Incoming chat is routed to the AI Agent – Trend Explorer node: First, the agent triggers the Workflow – YouTube Search tool to gather the latest trending topics and keywords from YouTube. Next, the agent supplies this real-time YouTube data to the OpenAI Chat Model for deep analysis, trend interpretation, and unique insights. To provide context-aware answers and track ongoing interests, the agent also references a Simple Memory module, recalling past queries, and user instructions. Finally, the result is a fast, data-driven, and smart trend report delivered instantly to your chat. How To Set Up Download the workflow package (including 2 .json files) and import it into your n8n interface. Set up necessary access in the following components of the AI Agent - Trend Explorer node: OpenAI Chat Model: allows API connection for trend insights. Workflow – YouTube Search: searches for trending videos based on the query. Simple Memory (optional): enhances experience for ongoing sessions. Start by sending a chat message on n8n. Check the response from the AI agent in the same chat box. Ask follow-ups, explore deeper, or trigger new searches - all in one chat thread. Requirements n8n instance (self-hosted or cloud). Set up API access to OpenAI Chat Model for chat-based AI. How To Customize Connect to your favorite chat platforms**: Easily integrate with additional chat triggers such as Telegram, Slack, or your preferred messaging app. Choose your preferred AI model**: If you want a different viewpoint, simply swap OpenAI Chat Model for Google Gemini, Claude, or any compatible LLM model in your workflow. Upgrade memory for smarter conversations: For long-term recall or more advanced, context-aware chats, replace **Simple Memory with a vector database like Pinecone or Redis. Need Help? If you’d like this workflow customized to fit your tools and platforms availability, 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 support, 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 has been updated to support the Query feature added to the Zendesk node in 0.144.0 This workflow will post all New and Open tickets without an agent assigned to a Slack channel on a schedule. The function node is used in this example to merge multiple inputs into one output message which is then used as the Slack message. The output in Slack will be similar to the below message, The "TICKET_ID" will be a link to the ticket. > Unassigned Tickets TICKET_ID [STATUS] - TICKET_SUBJECT Usage Update the Cron schedule, The default value is 16:30 daily. Update the Credentials in the Zendesk nodes Update the Credentials and Channel in the Slack Node Grab a coffee and enjoy! Zendesk Query In the Zendesk node we are using the query assignee:none status<pending this returns all New and Open tickets with no assignee allowing us to remove the extra nodes.

What it does This is a simplistic demo workflow showing how to extract a license plate number from an image of a car submitted via a form – or in more general terms showcasing how you can: use a form trigger to upload files and feed it into an LLM use a changeable LLM model for image-to-text analysis Set up steps Import the workflow Ensure you have registered and account, purchased some credits and created and API key for OpenRouter.ai Create/adapt the OpenRouter credential with your indivial API key for OpenRouter "Test workflow" and submit an image of a car with license plate to extract its number How to adapt By changing the "prompt" in th "Settings" node you can quickly adapt this exemplatory workflow to other image-to-text use cases, such as: summarization: "summarize what's seen in the image" location finding: "identify the location where the image was taken" text extraction: "extract all text from the image and return it as markdown" Thanks to using OpenRouter, you also can quickly experiment with finding good model choices by simply changing the "model" in the "Settings" node. The following models gave good results for this demo use-case: google/gemini-2.0-flash-001 meta-llama/llama-3.2-90b-vision-instruct openai/gpt-4o The llama-3.2-11b and even claude-3.5-sonnet didn't recognize all characters in all test images. Using a generic LLM-model offers a quick way of prototyping an image-to-text application. For specific use cases in serious and scalable production deployments, consider using an API based service specifically made to that purpose, such as: Google Cloud Vision API Microsoft Azure Computer Vision Azure AI Document Intelligence Amazon Textract

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