Workflow Templates

This template aims to perform Q&A on data retrieved from another n8n workflow. Since that workflow can be used to retrieve any data from any service, this template can be used to ask questions about any data. It uses a manual trigger, various AI nodes, and an OpenAI Chat Model to extract and provide relevant information based on a specific query. Note that to use this template, you need to be on n8n version 1.19.4 or later.

Twitter Virtual AI Influencer Workflow Template This n8n workflow template empowers creators to launch a virtual AI influencer that tweets regularly, engaging audiences with a unique niche, writing style, and inspiration. By automating content creation and posting, it ensures a consistent and natural online presence, tailored to your specific influencer profile. Features Scheduled Posting**: Automates tweet posting every 6 hours, with randomized posting minutes to mimic natural activity. On-Demand Posting**: Offers flexibility with manual trigger options for immediate content sharing. Influencer Profile Configuration**: Customize your virtual influencer by defining a target niche, personal writing style, and sources of inspiration. Content Generation**: Leverages advanced AI to craft tweets that resonate with your audience, aiming for viral engagement. Tweet Validation**: Ensures all generated content adheres to Twitter's character limit, maintaining quality and relevance. Workflow Steps Schedule Posting: Configured to post every 6 hours, this step introduces randomness in posting time to simulate human behavior. Trigger Posting Manually: Provides an option to manually initiate a tweet, offering control over the timing of your content. Configure Influencer Profile: Set up your influencer's niche, style, and inspiration to guide the AI in generating targeted content. Generate Tweet Content: Utilizes a sophisticated AI model to produce engaging tweets based on the configured profile. Validate Tweet: Checks if the generated tweet meets Twitter's length constraints, ensuring all content is ready for posting. Post Tweet: Finalizes the process by sharing the AI-generated tweet to your designated Twitter account. Configuration Notes Niche**: Define a specific area of interest, such as "Modern Stoicism," to focus your influencer's content. Writing Style**: Customize the tone and style of the tweets to reflect a personal touch, enhancing relatability. Inspiration**: Input sources of inspiration, including books and philosophies, to steer the content generation process. Getting Started To deploy this template: Import the workflow into your n8n workspace. Customize the influencer profile settings to match your desired niche, style, and inspiration. Connect your Twitter account through the provided OAuth2 credentials setup. Activate the workflow to start building your virtual influencer's presence on Twitter. Embrace the power of AI to create a distinctive and engaging virtual influencer, captivating your audience with minimal effort.

Video Demo: Click here to see a video of this workflow in action. Summary Description: The "IT Department Q&A Workflow" is designed to streamline and automate the process of handling IT-related inquiries from employees through Slack. When an employee sends a direct message (DM) to the IT department's Slack channel, the workflow is triggered. The initial step involves the "Receive DMs" node, which listens for new messages. Upon receiving a message, the workflow verifies the webhook by responding to Slack's challenge request, ensuring that the communication channel is active and secure. Once the webhook is verified, the workflow checks if the message sender is a bot using the "Check if Bot" node. If the sender is identified as a bot, the workflow terminates the process to avoid unnecessary actions. If the sender is a human, the workflow sends an acknowledgment message back to the user, confirming that their query is being processed. This is achieved through the "Send Initial Message" node, which posts a simple message like "On it!" to the user's Slack channel. The core functionality of the workflow is powered by the "AI Agent" node, which utilizes the OpenAI GPT-4 model to interpret and respond to the user's query. This AI-driven node processes the text of the received message, generating an appropriate response based on the context and information available. To maintain conversation context, the "Window Buffer Memory" node stores the last five messages from each user, ensuring that the AI agent can provide coherent and contextually relevant answers. Additionally, the workflow includes a custom Knowledge Base (KB) tool (see that tool template here) that integrates with the AI agent, allowing it to search the company's internal KB for relevant information. After generating the response, the workflow cleans up the initial acknowledgment message using the "Delete Initial Message" node to keep the conversation thread clean. Finally, the generated response is sent back to the user via the "Send Message" node, providing them with the information or assistance they requested. This workflow effectively automates the IT support process, reducing response times and improving efficiency. To quickly deploy the Knowledge Ninja app in Slack, use the app manifest below and don't forget to replace the two sample urls: { "display_information": { "name": "Knowledge Ninja", "description": "IT Department Q&A Workflow", "background_color": "#005e5e" }, "features": { "bot_user": { "display_name": "IT Ops AI SlackBot Workflow", "always_online": true } }, "oauth_config": { "redirect_urls": [ "Replace everything inside the double quotes with your slack redirect oauth url, for example: https://n8n.domain.com/rest/oauth2-credential/callback" ], "scopes": { "user": [ "search:read" ], "bot": [ "chat:write", "chat:write.customize", "groups:history", "groups:read", "groups:write", "groups:write.invites", "groups:write.topic", "im:history", "im:read", "im:write", "mpim:history", "mpim:read", "mpim:write", "mpim:write.topic", "usergroups:read", "usergroups:write", "users:write", "channels:history" ] } }, "settings": { "event_subscriptions": { "request_url": "Replace everything inside the double quotes with your workflow webhook url, for example: https://n8n.domain.com/webhook/99db3e73-57d8-4107-ab02-5b7e713894ad", "bot_events": [ "message.im" ] }, "org_deploy_enabled": false, "socket_mode_enabled": false, "token_rotation_enabled": false } }

This n8n workflow template uses community nodes and is only compatible with the self-hosted version of n8n. Export workflows with readable names, tagged for different environments To ensure understandable workflow exports, ease of use in delivery pipelines, and a better developer experience, this workflow helps with exporting workflows. Inner workings First, the workflow ensures that the directory structure for storing the workflows is correct. Exports all workflows. Next, it processes all workflow files and stores them with readable names. Based on tags, it will also export to dev and prod folders for easy commit and usage in a delivery pipeline. Configration No special setup is required for readable exporting. Usage Create a workflow and tag it with 'Auto deploy to dev' Run the workflow, this will create the needed folders and workflows with readable names. Commit these in your version control. Have a CICD pipeline build an n8n container —see the attached Dockerfile. Check our Auto Starter workflow for auto-starting workflows after deployment. CI/CD Bonus: Attached are two nodes with some example configuration on building your own automated n8n deployment. A Dockerfile, to get the new entrypoint and exported workflows packaged in the container. An updated entrypoint to build your own container, import the workflows, and run the Auto Starter. Set the following environment variables: STARTUP_WORKFLOWS_LOAD_LOCATION: to specify the folder to import from and distinguish between environments. STARTUP_WORKFLOW_ID: the ID of the workflow to run after starting n8n. > Note: The 'Instance Started' n8n trigger won't work, as all workflows are disabled upon import.

🔎 Who is this for? This workflow is designed for podcast creators, content marketers, and video producers who want to convert YouTube videos into podcast-ready scripts. It's perfect for anyone repurposing long-form content to reach audio-first audiences without manual effort. 🧠 What problem is this workflow solving? Creating podcast scripts from YouTube videos manually is time-consuming. This workflow automates the process by pulling transcripts, cleaning the text, organizing the dialogue, summarizing the key points, and saving everything in one place. It removes the need for manual transcription, formatting, and structuring. ⚙️ What this workflow does This workflow uses Dumpling AI and GPT-4o to automate the transformation of YouTube video transcripts into polished podcast scripts. Here's how it works: RSS Feed Trigger Monitors a YouTube RSS feed for new video uploads. When a new video is detected, the workflow begins automatically. Get YouTube Transcript (Dumpling AI) Uses Dumpling AI's get-youtube-transcript endpoint to extract the full transcript from the video URL. Generate Podcast Script with GPT-4o GPT-4o receives the transcript and generates a structured JSON output including: Cleaned transcript with filler words removed Speaker labels for clarity A short, engaging podcast title A concise summary of the episode Save to Airtable The structured data (title, summary, cleaned transcript) is saved to Airtable for easy review, editing, or publishing. This automation is an ideal workflow for repurposing video content into audio-friendly formats, cutting down production time while increasing content output across platforms.

From PDF to Powerful Blog Posts: AI-Powered Content Transformation Turn complex documents into engaging digital content that drives results. This n8n Workflow uses AI to transforms lengthy PDFs into compelling blog posts that attract and retain readers while you focus on strategic initiatives. Time-Saving Innovation 🚅Lightning-Fast Processing Transform lengthy documents into polished blog content in under 1 minute, eliminating hours of manual work. Our system handles the heavy lifting, delivering up to a 95% reduction in content production time. 📱Intelligent Analysis The AI engine identifies and extracts key insights, organizing information for maximum impact. Each document undergoes comprehensive analysis to ensure no valuable content is overlooked. Advanced Content Optimization ✍️Dynamic Writing Styles Possible Adjust the prompt for multiple tone options: Professional for corporate communications Conversational for engaging blogs Thought leadership for industry authority 📊SEO-Ready Content Potential Adjust the prompt to automatically optimized for search engines, incorporating relevant keywords and semantic structure to improve visibility and drive organic traffic. Ideal Applications 🤼Content Marketing Teams Scale content production without sacrificing quality or consistency. Perfect for teams looking to maintain a robust publishing schedule while maximizing resource efficiency. 🏫Academic Communication Help researchers and institutions share complex findings with broader audiences through accessible, engaging content that maintains academic integrity. 🧑‍💻Digital Publishers Streamline the content transformation process while ensuring each piece meets modern digital standards and reader expectations. Transform your content strategy with an intelligent system that delivers consistent, high-quality results while dramatically reducing production time.

What This Workflow Does Step 1: Analyzes your recent tweets for personality & style Step 2: Generates strategic keywords based on your profile Step 3: Searches for trending tweets in your niche Step 4: Creates personalized responses & original tweets Step 5: Displays results in beautiful HTML format How to Use Execute the workflow Fill out the form with: Your X username (from your URL) Your goals on X (multiple selection) Optional additional info Submit the form Wait for processing (2-3 minutes) Double-click the "Click to show Result" node example clip can be found here

This workflow includes advanced features like text summarization and tokenization, it's ideal for automating document processing tasks that require parsing and summarizing text data from Google Drive. To use this template, you need to be on n8n version 1.19.4 or later.

Audio Generator – Documentation 🎯 Purpose: Generate audio files from text scripts stored in Google Drive. 🔁 Flow: Receive repo IDs. Fetch text scripts. Generate .wav files using local Bark model. Upload back to Drive. 📦 Dependencies: Python script: /scripts/generate_voice.py Bark (voice generation system) n8n instance with access to local shell Google Drive OAuth2 credentials ✏️ Notes: Script filenames must end with .txt Only works with plain text No external API used = 100% free 📦 /scripts/generate_voice.py: import sys import torch import numpy import re from bark import SAMPLE_RATE, generate_audio, preload_models from scipy.io.wavfile import write as write_wav Patch to allow numpy._core.multiarray.scalar during loading torch.serialization.add_safe_globals([numpy._core.multiarray.scalar]) Monkey patch torch.load to force weights_only=False _original_torch_load = torch.load def patched_torch_load(f, args, *kwargs): if 'weights_only' not in kwargs: kwargs['weights_only'] = False return _original_torch_load(f, args, *kwargs) torch.load = patched_torch_load Preload Bark models preload_models() def split_text(text, max_len=300): Split on punctuation to avoid mid-sentence cuts sentences = re.split(r'(?<=[.?!])\s+', text) chunks = [] current = "" for sentence in sentences: if len(current) + len(sentence) < max_len: current += sentence + " " else: chunks.append(current.strip()) current = sentence + " " if current: chunks.append(current.strip()) return chunks Input text file and output path input_text_path = sys.argv[1] output_wav_path = sys.argv[2] with open(input_text_path, 'r', encoding='utf-8') as f: full_text = f.read() voice_preset = "v2/en_speaker_7" chunks = split_text(full_text) Generate and concatenate audio chunks audio_arrays = [] for chunk in chunks: print(f"Generating audio for chunk: {chunk[:50]}...") audio = generate_audio(chunk, history_prompt=voice_preset) audio_arrays.append(audio) Merge all audio chunks final_audio = numpy.concatenate(audio_arrays) Write final .wav file write_wav(output_wav_path, SAMPLE_RATE, final_audio) print(f"Full audio generated at: {output_wav_path}") `

This workflow provides automated access to the Notdaniel Voxtral Small 24B 2507 AI model through the Replicate API. It saves you time by eliminating the need to manually interact with AI models and provides a seamless integration for audio generation tasks within your n8n automation workflows. Overview This workflow automatically handles the complete audio generation process using the Notdaniel Voxtral Small 24B 2507 model. It manages API authentication, parameter configuration, request processing, and result retrieval with built-in error handling and retry logic for reliable automation. Model Description: Voxtral Small is an enhancement of Mistral Small 3 that incorporates state-of-the-art audio input capabilities and excels at speech transcription, translation and audio understanding. Key Capabilities AI-driven audio generation and processing** High-quality sound synthesis** Advanced audio manipulation tools** Tools Used n8n**: The automation platform that orchestrates the workflow Replicate API**: Access to the Notdaniel/voxtral-small-24b-2507 AI model Notdaniel Voxtral Small 24B 2507**: The core AI model for audio generation Built-in Error Handling**: Automatic retry logic and comprehensive error management How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Replicate API: Add your Replicate API token to the 'Set API Token' node Customize Parameters: Adjust the model parameters in the 'Set Audio Parameters' node Test the Workflow: Run the workflow with your desired inputs Integrate: Connect this workflow to your existing automation pipelines Use Cases Music Production**: Generate background music and audio tracks Podcast Enhancement**: Create intro/outro music and sound effects Audio Content**: Produce voiceovers and audio narration Sound Design**: Generate custom audio for games and applications Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Replicate API**: https://replicate.com (Sign up to access powerful AI models) #n8n #automation #ai #replicate #aiautomation #workflow #nocode #audiogeneration #aiaudio #soundgeneration #musicai #audioautomation #machinelearning #artificialintelligence #aitools #automation #digitalart #contentcreation #productivity #innovation

How It Works Form Submission: The workflow starts with the On form submission node, which triggers when a user submits a contact form. The form collects the user's name, email, and message. Text Classification: The Text Classifier node uses an AI model (GPT-4) to classify the submitted message into one of the predefined categories: Request Quote: For quote requests. Product info: For general product inquiries. General problem: For issues or problems related to products. Order: For questions about placed orders. Other: For any messages that don’t fit the above categories. Email Routing: Based on the classification, the workflow routes the message to the appropriate department via email: Prod. Dep.: For product-related inquiries. Quote Dep.: For quote requests. Gen. Dep.: For general problems. Order Dep.: For order-related questions. Other Dep.: For all other inquiries. Each email includes the user's name, email, message, and the classified category. Data Logging: The workflow logs the form submission and classification results into a Google Sheets document. Each department has its own sheet where the data is appended, including: User’s name, email, and message. Submission date and time. Assigned category. Email recipient details. AI Model Integration: The OpenAI node provides the AI model (GPT-4) used by the Text Classifier to classify the messages. The model is instructed to classify the text into one of the predefined categories without additional explanations. Set Up Steps Configure the Form Trigger: Set up the On form submission node to collect user inputs (name, email, and message) and trigger the workflow. Set Up the Text Classifier: Configure the Text Classifier node to use the OpenAI model (GPT-4) for text classification. Define the categories and their descriptions (e.g., "Request Quote", "Product info", etc.). Set the fallback category to "Other" for unclassifiable messages. Configure Email Sending: Set up the Email Send nodes for each department (Prod. Dep., Quote Dep., Gen. Dep., Order Dep., Other Dep.). Configure the email subject, body, and reply-to address using the form data and classification results. Ensure SMTP credentials are correctly configured for sending emails. Set Up Google Sheets Integration: Configure the Google Sheets nodes to append data to the appropriate sheets for each department. Map the form data (name, email, message, date, category, and recipient) to the corresponding columns in the Google Sheets document. Test the Workflow: Submit a test form to ensure the workflow correctly classifies the message, sends the email to the right department, and logs the data in Google Sheets. Verify that the OpenAI model is classifying messages accurately. Activate the Workflow: Once tested, activate the workflow to automate the process of handling contact form submissions. Key Features Automated Classification**: Uses AI to classify messages into relevant categories, reducing manual effort. Email Routing**: Sends emails to the appropriate department based on the classification. Data Logging**: Logs all form submissions and classification results in Google Sheets for tracking and analysis. Scalability**: Easily adaptable to additional categories or departments by modifying the workflow. This workflow is ideal for eCommerce businesses or customer support teams looking to automate and streamline the handling of contact form submissions. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Are you looking to create a counseling chatbot that provides emotional support and mental health guidance through the LINE messaging platform ? This guide will walk you through connecting LINE with powerful AI language models like GPT-4 to build a chatbot that supports users in navigating their emotions, offering 24/7 conversational therapy and accessible mental health resources . By leveraging LINE's webhook integration and Azure OpenAI , this template allows you to design a chatbot that is both empathetic and efficient, ensuring users receive timely and professional responses. Whether you're a developer, counselor, or business owner, this guide will help you create a customizable counseling chatbot tailored to your audience's needs. Who Is This Template For? Developers who want to integrate AI-powered chatbots into the LINE platform for mental health applications. Counselors & Therapists looking to expand their reach and provide automated emotional support to clients outside of traditional sessions. Businesses & Organizations focused on improving mental health accessibility and offering innovative solutions to their users. Educators & Nonprofits seeking tools to provide free or low-cost counseling services to underserved communities. How this work? Line Webhook to receive new message Send loading animation in Line Check if the input is text or not Send the text as prompt in chat model (GPT 4o) Reply the message to user (you'll need 'edit field' to format it before reply) Pre-Requisites You have access to the LINE Developers Console. An Azure OpenAI account with necessary credentials. Set-up To receive messages from LINE, configure your webhook: Set up a webhook in LINE Developer Console. Copy the Webhook URL from the Line Chatbot node and paste it into the LINE Console. Ensure to remove any 'test' part when moving to production. The loading animation reassures users that the system is processing their request. Authorize using header authorization Message Handling Use the Check Message Type IsText? node to verify if the incoming message is text. If the message type is text, proceed with ChatGPT processing; otherwise, send a reply indicating non-text inputs are not supported. AI Agent Configuration Define the system message within the AI Agent node to guide the conversation based on desired interaction principles. Connect the Azure OpenAI Chat Model to the AI Agent. Formatting Responses Ensure responses are properly formatted before sending them back to the user. Reply Message Use the ReplyMessage - Line node to send the formatted response. Ensure proper header authorization using Bearer tokens.