Workflow Templates

PersonalNotesAssistant – Organize and Understand Your Thoughts with Local AI PersonalNotesAssistant is an offline-capable, AI-powered agent that helps you store, summarize, retrieve, and reflect on your personal notes and voice memos — all processed locally and sent via Telegram. Built to run efficiently on a Raspberry Pi 5, this agent supports a variety of note-taking styles and acts as your private memory extension. 🧠 What It Can Do Accept voice or text notes via Telegram Transcribe audio messages into clean, structured text (using Whisper) Automatically summarize or categorize notes with a local LLM Answer questions based on your past notes Retrieve relevant entries by topic, date, or keyword Help you journal or reflect by asking follow-up questions Work completely offline — no cloud or external APIs 🔧 How It Works Capture Notes via Telegram You send a voice message or text to your Telegram bot. The assistant supports both quick thoughts and long-form content. Transcription with Whisper (Local) If the input is a voice message, it is transcribed into text using Whisper running locally on your Raspberry Pi. AI Summarization & Tagging The transcribed or typed note is sent to LLaMA 3.2 via Ollama, which summarizes it, suggests tags, and stores it with metadata (e.g., timestamp, mood, theme). Storage & Retrieval Notes are stored in a local database (e.g., SQLite or JSON). You can later query the assistant with prompts like: “What did I say about stress last week?” “Summarize my ideas from this month.” “Show notes tagged with 'travel'.” Follow-Up & Reflection The agent can optionally engage with reflective prompts to help you deepen your thoughts or gain insight from what you’ve recorded. 💡 Use Cases Track personal growth, habits, or therapy progress Create voice memos while walking or commuting Maintain a structured journal without typing Use as a second brain to help you remember and revisit important thoughts 🔐 Privacy by Default Everything runs locally: No notes are uploaded to cloud platforms No audio is sent to third-party transcription services No LLM processing happens outside your device Ideal for privacy-minded users, psychologists, researchers, or digital minimalists who want AI assistance without surveillance. ⚙️ Technical Stack Raspberry Pi 5: Low-power edge device Whisper (local): For voice-to-text conversion Ollama + LLaMA 3.2: For summarization, classification, and retrieval Telegram Bot API: For input/output Custom Database (e.g., JSON/SQLite): For storing and querying notes 🧪 Real-Life Use This agent is actively used daily by the developer to log ideas, emotions, and plans. It has proven effective for lightweight journaling and context-aware memory assistance, even when offline.

Use case This workflow is designed for e-commerce brands and content teams who: Need to scale SEO content production without sacrificing quality Want to eliminate manual keyword filtering (saves 10+ hours/week) Aim to dominate niche search terms (e.g., "vegan leather crossbody bags") What this workflow does Automates the end-to-end process from keyword discovery to publish-ready articles: Keyword Harvesting: Pulls 1,000+ keywords/day from SEMrush/Ahrefs Smart Filtering:Blocks competitor brands (e.g., "Zara alternatives") Detects irrelevant demographics ("kids", "petite") AI Content Generation:Flags non-compliant colors (non-black/white terms) Multi-Channel Output: Formats content for blogs, product descriptions, and email campaigns setup Add Google,SEMrush and OpenAI credentials Set the rules excel of google drive Test workflow by testing workflow Review generated opportunity report in Google Sheets How to adjust this template Change scenario: Replace the rules and define different target

Ever wanted to just tell your 3d printer what to do remotely? This game changer let's you converse with OpenAI agents to manage OctoPrint connected 3d printers. Great for remote management and monitoring with easy to read responses. Query what jobs are available, Load a print job (upload workflow in development, will be released soon for community use), start the print job and request detailed reports on your printers performance as it runs. Chat > AI > OctoPrint API > Your 3d Printer ++Example 1:++ Human Input: "What print job is running?" AI Response: Current Print Job Details, Job Name: Stand v1.gcode File Size: 2,533,233 bytes Estimated Print Time: Approximately 6 hours 15 minutes (22536 seconds) Progress: Completion: 1.97%, Current Position in File: 49,940 bytes, Time Spent Printing: 2,611 seconds (~43.5 minutes), Time Remaining: Approximately 5 hours 34 minutes (20,123 seconds), Status, Current State: Printing." ++Example 2++ Human Input: "Is my 3d Printer ok?" AI Response: "Status: Printing, Operational: Yes, ++Temperature:++ Bed Temperature: Actual: 50°C, Target: 50°C, Nozzle (Tool 0) Temperature: Actual: 200°C, Target: 200°C, ++Connection Status:++ Connection State: Currently connected to the printer, Port: /dev/ttyUSB0, Baudrate: 115200, Summary, Your 3D printer is currently operational and printing without errors. The bed and nozzle temperatures are both at their target values."

Disclaimer The Execute Command node is only supported on self-hosted (local) instances of n8n. Introduction KOKORO TTS - Kokoro TTS is a compact yet powerful text-to-speech model, currently available on Hugging Face and GitHub. Despite its modest size—trained on less than 100 hours of audio—it delivers impressive results, consistently topping the TTS leaderboard on Hugging Face. Unlike larger systems, Kokoro TTS offers the advantage of running locally, even on devices without GPUs, making it accessible for a wide range of users. Who will benefit from this integration? This will be useful for video bloggers, TikTokers, and it will also enable the creation of a free voice chat bot. Currently, TTS models are mostly paid, but this integration will allow for fully free voice generation. The possibilities are limited only by your imagination. Note Unfortunately, we can't interact with the KOKORO API via browser URL (GET/POST), but we can run a Python script through n8n and pass any variables to it. In the tutorial, the D drive is used, but you can rewrite this for any paths, including the C drive. Step 1 You need to have Python installed. link Also, download and extract the portable version of KOKORO from GitHub. Create a file named voicegen.py with the following code in the KOKORO folder: (C:\KOKORO). As you can see, the output path is: (D:\output.mp3). import sys import shutil from gradio_client import Client Set UTF-8 encoding for stdout sys.stdout.reconfigure(encoding='utf-8') Get arguments from command line text = sys.argv[1] # First argument: input text voice = sys.argv[2] # Second argument: voice speed = float(sys.argv[3]) # Third argument: speed (converted to float) print(f"Received text: {text}") print(f"Voice: {voice}") print(f"Speed: {speed}") Connect to local Gradio server client = Client("http://localhost:7860/") Generate speech using the API result = client.predict( text=text, voice=voice, speed=speed, api_name="/generate_speech" ) Define output path output_path = r"D:\output.mp3" Move the generated file shutil.move(result[1], output_path) Print output path print(output_path) Step 2 Go to n8n and create the following workflow. Step 3 Edit Field Module. { "voice": "af_sarah", "text": "Hello world!" } Step 4 We’ll need an Execute Command module with the command: python C:\KOKORO\voicegen.py “{{ $json.text }}” “{{ $json.voice }}” 1 Step 5 The script is already working, but to listen to it, you can connect a Binary module with the path to the generated MP3 file D:/output.mp3 Step 6 Click “Text workflow” and enjoy the result. There are more voices and accents than in ChatGPT, plus it’s free. P.S. If you want, there is a detailed tutorial on my blog.

This workflow powers the Binance Spot Market Quant AI Agent, acting as the Financial Market Analyst. It fuses real-time market structure data (price, volume, kline) with multiple timeframe technical indicators (15m, 1h, 4h, 1d) and returns a structured trading outlook—perfect for intraday and swing traders who want actionable analysis in Telegram. 🔗 Requires the following sub-workflows to function: • Binance SM 15min Indicators Tool • Binance SM 1hour Indicators Tool • Binance SM 4hour Indicators Tool • Binance SM 1day Indicators Tool • Binance SM Price/24hStats/Kline Tool ⚙️ How It Works Triggered via webhook (typically by the Quant AI Agent). Extracts user symbol + timeframe from input (e.g., "DOGE outlook today"). Calls all linked sub-workflows to retrieve indicators + live price data. Merges the data and formats a clean trading report using GPT-4o-mini. Returns HTML-formatted message suitable for Telegram delivery. 📥 Sample Input { "message": "SOLUSDT", "sessionId": "654321123" } ✅ Telegram Output Format 📊 SOLUSDT Market Snapshot 💰 Price: $156.75 📉 24h Stats: High $160.10 | Low $149.00 | Volume: 1.1M SOL 🧪 4h Indicators: • RSI: 58.2 (Neutral-Bullish) • MACD: Crossover Up • BB: Squeezing Near Upper Band • ADX: 25.7 (Rising Trend) 📈 Resistance: $163 📉 Support: $148 🔍 Use Cases | Scenario | Outcome | | ------------------------------- | --------------------------------------------------------- | | User asks for “BTC outlook” | Returns 1h + 4h + 1d indicators + live price + key levels | | Telegram bot prompt: “DOGE now” | Returns short-term 15m + 1h analysis snapshot | | Strategy trigger inside n8n | Enables other workflows to consume structured signal data | 🎥 Watch Tutorial: 🧾 Licensing & Attribution © 2025 Treasurium Capital Limited Company Architecture, prompts, and trade report structure are IP-protected. No unauthorized rebranding or redistribution permitted. 🔗 For support: LinkedIn – Don Jayamaha

Este workflow es para trabajar con tratamiento de texto usando n8n y poder iniciarte en como funciona. How To, Paso a Paso: https://comunidad-n8n.com/tratamiento-de-textos/ Comunidad de telegram: https://t.me/comunidadn8n

This workflow shows how you can get your OpenAI assistant to call an n8n workflow as a tool. Since you can put almost any functionality in an n8n workflow, this means you can give your assistant access to almost any data source. Note that to use this template, you need to be on n8n version 1.19.4 or later.

Imagine having an AI chatbot on Slack that seamlessly integrates with your company’s workflow, automating repetitive requests. No more digging through emails or documents to find answers about IT requests, company policies, or vacation days—just ask the bot, and it will instantly provide the right information. With its 24/7 availability, the chatbot ensures that team members get immediate support without waiting for a colleague to be online, making assistance faster and more efficient. Moreover, this AI-powered bot serves as a central hub for internal communication, allowing everyone to quickly access procedures, documents, and company knowledge without searching manually. A simple Slack message is all it takes to get the information you need, enhancing productivity and collaboration across teams. How It Works Slack Trigger: The workflow starts when a user mentions the AI bot in a Slack channel. The trigger captures the message and forwards it to the AI Agent. AI Agent Processing: The AI Agent, powered by Anthropic's Claude 3.7 Sonnet model, processes the query. It uses Retrieval-Augmented Generation (RAG) to fetch relevant information from the company’s internal knowledge base stored in Qdrant (a vector database). A Simple Memory buffer retains recent conversation context (last 10 messages) for continuity. Knowledge Retrieval: The RAG tool searches Qdrant’s vector store using OpenAI embeddings to find the most relevant document chunks (top 10 matches). Response Generation: The AI synthesizes the retrieved data into a concise, structured response (1-2 sentences for the answer, 2-3 supporting details, and a source citation). The response is formatted in Slack-friendly markdown (bullet points, blockquotes) and sent back to the user. Set Up Steps Prepare Qdrant Vector Database: Create a Qdrant collection via HTTP request (Create collection node). Optionally, refresh/clear the collection (Refresh collection node) before adding new documents. Load Company Documents: Fetch files from a Google Drive folder (Get folder → Download Files). Process documents: Split text into chunks (Token Splitter) and generate embeddings (Embeddings OpenAI2). Store embeddings in Qdrant (Qdrant Vector Store1). Configure Slack Bot: Create a Slack bot via Slack API with required permissions Add the bot to the desired Slack channel and note the channelId for the workflow. Deploy AI Components: Connect the AI Agent to Anthropic’s model, RAG tool, and memory buffer. Ensure OpenAI embeddings are configured for both RAG and document processing. Test & Activate: Use the manual trigger (When clicking ‘Test workflow’) to validate document ingestion. Activate the workflow to enable real-time Slack interactions. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Enrich your company lists with OpenAI GPT-3 ↓ You’ll get valuable information such as: Market (B2B or B2C) Industry Target Audience Value Proposition This will help you to: add more personalization to your outreach make informed decisions about which accounts to target I've made the process easy with an n8n workflow. Here is what it does: Retrieve website URLs from Google Sheets Extract the content for each website Analyze it with GPT-3 Update Google Sheets with GPT-3 data

Task: Control your data flow with rate limits and external cues Main use cases: Control the rate of items flow into one or more services in your workflow Wait for external events to occur before continuing with the rest of the workflow

This is a workflow that tries to answer user queries using the standard GPT-4 model. If it can't answer, it sends a message to Slack to ask for human help. It prompts the user to supply an email address. This workflow is used in Advanced AI examples | Ask a human in the documentation. To use this workflow: Load it into your n8n instance. Add your credentials as prompted by the notes. Configure the Slack node to use your Slack details, or swap out Slack for a different service.

This is a workflow that sends daily astronomy picture of the day using the NASA node to a channel on Telegram. Cron node: The Cron node triggers the workflow daily at 8 PM. You can update the time in the Cron node to trigger the workflow at your desired time. NASA node: After the Cron node triggers the workflow, the NASA node fetches the Astronomy Picture of the Day from the NASA API. You can also get the binary file of the image. Toggle Download Image to true to get the file. Telegram node: The Telegram node sends the image to a Telegram channel. If you want to share the image on another platform, you can replace the Telegram node with the node of that platform. For example, if you want to post the image on a channel on Slack, replace the Telegram node with the Slack node. You can learn to build this workflow on the documentation page of the NASA node.