Workflow Templates

This workflow is a template example for making Discord GPT-powered bots. Incoming user requests are analysed and categorised via OpenAI node with the help of a hand-crafted prompt. The response message is then routed to a different Discord channel.

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.

This workflow turns trending news into thoughtful first-person commentary for platforms like LinkedIn. It uses Dumpling AI’s News Search and Scraping APIs to find and extract article content, then feeds the cleaned text to GPT-4o to write personalized insights. The final output is saved back to Google Sheets as a draft for easy review or posting. ✅ What this workflow does Triggers daily using a Schedule node. Fetches a list of content topics from a Google Sheet. Uses Dumpling AI to search for relevant news articles based on each topic. Scrapes the article content with Dumpling AI’s /scrape endpoint. Cleans and aggregates the article content using a Code node. Generates first-person commentary with GPT-4o tailored for LinkedIn. Appends the generated post back to the Google Sheet next to its topic. 🧩 Nodes in this workflow Schedule Trigger**: Starts the workflow daily. Google Sheets (Read Topics)**: Pulls topic rows that don’t have a generated commentary yet. Split In Batches**: Processes each topic one at a time. Wait**: Adds a delay to manage API limits. HTTP Request (Search News)**: Uses Dumpling AI's /search-news to find relevant articles for the topic. Split Out**: Iterates over the list of article results. HTTP Request (Scrape Article)**: Extracts the full article text using Dumpling AI’s /scrape. Aggregate**: Collects and merges article content fields. Code (Clean Article)**: Strips links, formatting, and irrelevant text. OpenAI (GPT-4o)**: Generates a short, first-person LinkedIn post-style commentary using a custom prompt. Google Sheets (Write Back)**: Appends the final output next to the original topic in the sheet. 🧑‍💼 Who is this for? Founders, content creators, marketers, or **agency teams looking to maintain an active presence on LinkedIn or newsletters by sharing smart takes on industry trends. 💡 What problem does this solve? Most people want to comment on current events but don't have the time to summarize articles or write well-structured posts. This automation saves hours of manual work by: Finding the right article. Extracting and cleaning the content. Writing it in a natural, first-person voice using AI. ⚙️ What you need to use this: A Google Sheet with at least two columns: topic and generated commentary. A Dumpling AI API Key with access to the /search-news and /scrape endpoints. An OpenAI GPT-4o connection.

🔎 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

-- Disclaimer: This workflow uses a community node and therefore only works for self-hosted n8n users -- Transform YouTube videos into comprehensive summaries and structured analysis instantly. This n8n workflow automatically extracts, processes, and analyzes video transcripts to deliver clear, organized insights without watching the entire video. Time-Saving Features 🚀 Instant Processing Simply provide a YouTube URL and receive a structured summary within seconds, eliminating the need to watch lengthy videos. Perfect for research, learning, or content analysis. 🤖 AI-Powered Analysis Leverages GPT-4o-mini to analyze video transcripts, organizing key concepts and insights into a clear, hierarchical structure with main topics and essential points. Smart Processing Pipeline 📝 Automated Transcript Extraction Supports public YouTube video Handles multiple URL formats Extracts complete video transcripts automatically 🧠 Intelligent Content Organization Breaks down content into main topics Highlights key concepts and terminology Maintains technical accuracy while improving clarity Structures information logically with markdown formatting Perfect For 📚 Researchers & Students Quick comprehension of educational content and lectures without watching entire videos. 💼 Business Professionals Efficient analysis of industry talks, presentations, and training materials. 🎯 Content Creators Rapid research and competitive analysis of video content in your niche. Technical Implementation 🔄 Workflow Components Webhook endpoint for URL submission YouTube API integration for video details Transcript extraction system GPT-4 powered analysis engine Telegram notification system (optional) Transform your video content consumption with an intelligent system that delivers structured, comprehensive summaries while saving hours of viewing time.

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}") `