Workflow Templates

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automatically performs weekly keyword research and competitor analysis to discover trending keywords in your industry. It saves you time by eliminating the need to manually research keywords and provides a constantly updated database of trending search terms and opportunities. Overview This workflow automatically researches trending keywords for any specified topic or industry using AI-powered search capabilities. It runs weekly to gather fresh keyword data, analyzes search trends, and saves the results to Google Sheets for easy access and analysis. Tools Used n8n**: The automation platform that orchestrates the workflow Bright Data**: For accessing search engines and keyword data sources OpenAI**: AI agent for intelligent keyword research and analysis Google Sheets**: For storing and organizing keyword research data How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Bright Data: Add your Bright Data credentials to the MCP Client node Set Up OpenAI: Configure your OpenAI API credentials Configure Google Sheets: Connect your Google Sheets account and set up your keyword tracking spreadsheet Customize: Define your target topics or competitors for keyword research Use Cases SEO Teams**: Discover new keyword opportunities and track trending search terms Content Marketing**: Find trending topics for content creation and strategy PPC Teams**: Identify new keywords for paid advertising campaigns Competitive Analysis**: Monitor competitor keyword strategies and market trends Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #keywordresearch #seo #brightdata #webscraping #competitoranalysis #contentmarketing #n8nworkflow #workflow #nocode #seoresearch #keywordmonitoring #searchtrends #digitalmarketing #keywordtracking #contentautomation #marketresearch #trendingkeywords #keywordanalysis #seoautomation #keyworddiscovery #searchmarketing #keyworddata #contentplanning #seotools #keywordscraping #searchinsights #markettrends #keywordstrategy

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}") `

Welcome to my Airbnb Telegram Agent Workflow! This workflow creates an intelligent Telegram bot that helps users search and find Airbnb accommodations using natural language queries and voice messages. DISCLAIMER: This workflow only works with self-hosted n8n instances! You have to install the n8n-nodes-mcp-client Community Node! What this workflow does This workflow processes incoming Telegram messages (text or voice) and provides personalized Airbnb accommodation recommendations. The AI agent understands natural language queries, searches through Airbnb data using MCP tools, and returns mobile-optimized results with clickable links, prices, and key details. Key Features: Voice message support (speech-to-text and text-to-speech) Conversation memory for context-aware responses Mobile-optimized formatting for Telegram Real-time Airbnb data access via MCP integration This workflow has the following sequence: Telegram Trigger - Receives incoming messages from users Text or Voice Switch - Routes based on message type Voice Processing (if applicable) - Downloads and transcribes voice messages Text Preparation - Formats text input for the AI agent Airbnb AI Agent - Core logic that: Lists available MCP tools for Airbnb data Executes searches with parsed parameters Formats results for mobile display Response Generation - Sends formatted text response Voice Response (optional) - Creates and sends audio summary Requirements: Telegram Bot API**: Documentation Create a bot via @BotFather on Telegram Get bot token and configure webhook OpenAI API**: Documentation Used for speech transcription (Whisper) Used for chat completion (GPT-4) Used for text-to-speech generation MCP Community Client Node**: Documentation Custom integration for Airbnb data Requires MCP server setup with Airbnb/Airtable connection Provides tools for accommodation search and details Important: You need to set up an MCP server with Airbnb data access. The workflow uses MCP tools to retrieve real accommodation data, so ensure your MCP server is properly configured with the Airtable/Airbnb integration. Configuration Notes: Update the Telegram chat ID in the trigger for your specific bot Modify the system prompt in the Airbnb Agent for different use cases The workflow supports both individual users and can be extended for group chats Feel free to contact me via LinkedIn, if you have any questions!

📊 Dynamic Portfolio Advisor – Daily Stock Market Intelligence with Google Sheets Description: This advanced AI-powered n8n workflow automatically delivers a daily market intelligence briefing tailored to your stock holdings portfolio stored in Google Sheets. It uses real-time data from Perplexity AI, combines it with your portfolio, and generates personalized insights, risk alerts, and trade suggestions — all delivered via Telegram or any messaging app of your choice. For step-by-step build of workflows like this, check out: https://www.youtube.com/@Automatewithmarc ⚙️ How It Works: 🕒 Daily Trigger  Starts every day at a scheduled time (default: 10 AM) to fetch the most recent market data. 📈 Holdings Fetch  Reads your current portfolio dynamically from Google Sheets — no hardcoding required. 🧠 AI Analysis Agent  Combines: Market headlines Company-specific developments Macroeconomic updates  And analyzes how they might affect your holdings. 🔍 Perplexity Web Research Tool  Finds and summarizes the most relevant stock market news from the past 24 hours. 💬 Telegram Delivery  Sends a customized summary of: Market highlights Asset-specific impacts Opportunities and risks Actionable trade ideas (buy/sell/hold) 🛠️ Tools & Integrations: Google Sheets (live holdings feed) Perplexity AI (real-time market research) OpenAI GPT (financial summarization) Telegram (output, customizable) 💡 Use Cases: Portfolio-aware market intelligence Automated investor briefing assistant Risk alert + opportunity scanner Daily trade idea generator Finance bloggers or equity analysts streamlining prep work 📍Note: You can easily replace Telegram with Slack, Email, Notion, or any output tool supported by n8n. This template is perfect for active investors, financial advisors, or automation-savvy traders who want to turn AI and data into actionable daily signals.

Welcome to my Simple OpenAI Image Generator Workflow! This workflow creates an image with the new OpenAI image model "GPT-Image-1" based on a form input. This workflow has the following sequence: Form trigger (image prompt and image size input) Generate the Image via OpenAI API. Return the image to the input form for download. The following accesses are required for the workflow: OpenAI API access: Documentation Instructions Link your OpenAI Platform account in the “OpenAI Image Generation” node ("Credential Type") You can contact me via LinkedIn, if you have any questions: https://www.linkedin.com/in/friedemann-schuetz

This workflow provides automated access to the Ibm Granite Granite 3.3 8B Instruct 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 text generation tasks within your n8n automation workflows. Overview This workflow automatically handles the complete text generation process using the Ibm Granite Granite 3.3 8B Instruct 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: Granite-3.3-8B-Instruct is a 8-billion parameter 128K context length language model fine-tuned for improved reasoning and instruction-following capabilities. Key Capabilities Advanced text generation and processing** Natural language understanding and generation** Intelligent text manipulation and analysis** Tools Used n8n**: The automation platform that orchestrates the workflow Replicate API**: Access to the Ibm Granite/granite-3.3-8b-instruct AI model Ibm Granite Granite 3.3 8B Instruct**: The core AI model for text 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 Text Parameters' node Test the Workflow: Run the workflow with your desired inputs Integrate: Connect this workflow to your existing automation pipelines Use Cases Content Writing**: Generate articles, blogs, and marketing copy Code Generation**: Assist with programming and code documentation Text Analysis**: Process and analyze large volumes of text data Automated Communication**: Generate responses and communication templates 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 #textgeneration #nlp #aiwriting #textai #contentgeneration #aitext #machinelearning #artificialintelligence #aitools #automation #digitalart #contentcreation #productivity #innovation

Turn Your Ideas into Videos—Right from Google Sheets! This workflow helps you make cool 8-second videos using Fal.AI and Veo 3, just by typing your idea into a Google Sheet. You can even choose if you want your video to have sound or not. It’s super easy—no tech skills needed! Why use this? Just type your idea in a sheet—no fancy tools or uploads. Get a video link back in the same sheet. Works with or without sound—your choice! How does it work? You write your idea, pick the video shape, and say if you want sound (true or false) in the Google Sheet. n8n reads your idea and asks Fal.AI to make your video. When your video is ready, the link shows up in your sheet. What do you need? A Google account and Google Sheets connected with service account (check this link for reference) A copy of the following Google Spreadsheet: Spreadsheet to copy An OpenAI API key A Fal.AI account with some money in it That’s it! Just add your ideas and let the workflow make the videos for you. Have fun creating! if you have any questions, just contact me at max@nervoai.com

Try It Out! This n8n template helps you create SEO-optimized Blog Posts for your businesses website or for personal use. Whether you're managing a business or helping local restaurants improve their digital presence, this workflow helps you build SEO-Optimized Blog Posts in seconds using Google Autocomplete and People Also Ask (SerpAPI). Who Is It For? This is helpful for people looking to SEO Optimize either another person's website or their own. How It Works? You start with a list of blog inspirations in Google Sheets (e.g., “Best Photo Session Spots”). The workflow only processes rows where the “Status” column is not marked as “done”, though you can remove this condition if you’d like to process all rows each time. The workflow pulls Google Autocomplete suggestions and PAA questions using: A custom-built SEO API I deployed via Render (for Google Autocomplete + PAA), SerpAPI (for additional PAA data). These search insights are merged. For example, if your blog idea is “Photo Session Spots,” the workflow gathers related Google search phrases and questions users are asking. Then, GPT-4 is used to draft a full blog post based on this data. The finished post is saved back into your Google Sheet. How To Use Fill out the “Blog Inspiration” column in your Google Sheet with the topics you want to write about. Update the OpenAI prompt in the ChatGPT node to match your tone or writing style. (Tip: Add a system prompt with context about your business or audience.) You can trigger this manually, or replace it with a cron schedule, webhook, or other event. Requirements A SerpAPI account to get PAA An OpenAI account for ChatGPT Access to Google Sheets and n8n How To Set-Up? Your Google Sheet should have three columns: "Blog Inspiration", "Status" → set this to “done” when a post has been generated, "Blog Draft" → this is automatically filled by the workflow. To use the SerpAPI HTTP Request node: 1. Drag in an HTTP Request node, 2. Set the Method and URL depending on how you're using SerpAPI: Use POST to run the actor live on each request. Use GET to fetch from a static dataset (cheaper if reusing the same data). 3. Add query parameters for your SerpAPI key and input values. 4. Test the node. Refer to this n8n documentation for more help! https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolserpapi/. The “Autocomplete” node connects to a custom web service I built and deployed using Render. I wrote a Python script (hosted on GitHub) that pulls live Google Autocomplete suggestions and PAA questions based on any topic you send. This script was turned into an API and deployed as a public web service via Render. Anyone can use it by sending a POST request to: https://seo-api2.onrender.com/get-seo-data (the URL is already in the node). Since this is hosted on Render’s free tier, if the service hasn’t been used in the past ~15 minutes, it may “go to sleep.” When that happens, the first request can take 10–30 seconds to respond while it wakes up. Happy Automating!

Description This workflow automatically generates Facebook ad headlines for your product using OpenAI and evaluates their quality using custom AI-generated criteria. It ensures you get high‑quality, scroll‑stopping headlines without needing a copywriter. Overview This workflow captures a product description via a form, generates a Facebook ad headline, invents a scoring rubric, evaluates the headline against it, and optionally loops for revisions — all autonomously. Ideal for marketers and media buyers looking to scale creative testing. Tools Used n8n**: The automation platform that powers and orchestrates the entire workflow. OpenAI**: Used for headline generation, scoring criteria creation, and evaluation logic. (Optional)** Google Sheets / Notion / Email: For logging approved headlines or sharing results. How to Install Import the Workflow: Download the .json file and import it into your n8n instance. Connect OpenAI: Add your OpenAI credentials to the GPT nodes. Customize the Prompt (optional): Tweak the system prompt inside the Set_PromptForHeadline node. Add Output Handling (optional): Connect the “NO” path in the If_NeedMoreIterations node to Google Sheets, Slack, etc. (Optional) Add loop limits or storage logic to manage iterations or save results. Use Cases Media Buyers**: Generate and test hooks at scale with no creative bottlenecks. Solo Marketers**: Get high-converting headlines even without a copywriter. Agencies**: Streamline copy testing and evaluation in client campaigns. Startup Teams**: Automate creative generation during product launches or A/B tests. Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Hashtags #n8n #openai #automation #copywriting #facebookads #headlines #aicopy #promptengineering #marketingautomation #nocode #llm #creativeautomation #mediabuying #adtesting #adcreative #marketingtools #digitalmarketing #copytesting #scalablecreative #chatgpt #adhooks #growthmarketing #automatedworkflows #aiworkflow #creativeops #marketingops #growthtools

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.

Use n8n to bring data from any API to your AI. This workflow uses the Chat Trigger to provide the chat interface, and the Custom n8n Workflow Tool to call a second workflow that calls the API. The second workflow uses AI functionality to refine the API request based on the user's query. It then makes an API call, and returns the response to the main workflow. This workflow is used in Advanced AI examples | Call an API to fetch data in the documentation. To use this workflow: Load it into your n8n instance. Add your credentials as prompted by the notes. Requires n8n 1.28.0 or above

This workflow is designed to intelligently route user queries to the most suitable large language model (LLM) based on the type of request received in a chat environment. It uses structured classification and model selection to optimize both performance and cost-efficiency in AI-driven conversations. It dynamically routes requests to specialized AI models based on content type, optimizing response quality and efficiency. Benefits Smart Model Routing**: Reduces costs by using lighter models for general tasks and reserving heavier models for complex needs. Scalability**: Easily expandable by adding more request types or LLMs. Maintainability**: Clear logic separation between classification, model routing, and execution. Personalization**: Can be integrated with session IDs for per-user memory, enabling personalized conversations. Speed Optimization**: Fast models like GPT-4.1 mini or Gemini Flash are chosen for tasks where speed is a priority. How It Works Input Handling: The workflow starts with the "When chat message received" node, which triggers the process when a chat message is received. The input includes the chat message (chatInput) and a session ID (sessionId). Request Classification: The "Request Type" node uses an OpenAI model (gpt-4.1-mini) to classify the incoming request into one of four categories: general: For general queries. reasoning: For reasoning-based questions. coding: For code-related requests. google: For queries requiring Google tools. The classification is structured using the "Structured Output Parser" node, which enforces a consistent output format. Model Selection: The "Model Selector" node routes the request to one of four AI models based on the classification: Opus 4 (Claude 4 Sonnet): Used for coding requests. Gemini Thinking Pro: Used for reasoning requests. GPT 4.1 mini: Used for general requests. Perplexity: Used for search (Google-related) requests. AI Processing: The selected model processes the request via the "AI Agent" node, which includes intermediate steps for complex tasks. The "Simple Memory" node retains session context using the provided sessionId, enabling multi-turn conversations. Output: The final response is generated by the chosen model and returned to the user. Set Up Steps Configure Trigger: Ensure the "When chat message received" node is set up with the correct webhook ID to receive chat inputs. Define Classification Logic: Adjust the prompt in the "Request Type" node to refine classification accuracy. Verify the output schema in the "Structured Output Parser" node matches expected categories (general, reasoning, coding, google). Connect AI Models: Link each model node (Opus 4, Gemini Thinking Pro, GPT 4.1 mini, Perplexity) to the "Model Selector" node. Ensure credentials (API keys) for each model are correctly configured in their respective nodes. Set Up Memory: Configure the "Simple Memory" node to use the sessionId from the input for context retention. Test Workflow: Send test inputs to verify classification and model routing. Check intermediate outputs (e.g., request_type) to ensure correct model selection. Activate Workflow: Toggle the workflow to "Active" in n8n after testing. Need help customizing? Contact me for consulting and support or add me on Linkedin.