Workflow Templates

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

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."

🛡️📥 Telegram Invoice Agent → 🔎 OCR → 🤖 AI Parsing → 📄 Google Sheets + 🗂️ Drive What this workflow does 🤖 Captures invoices from Telegram and auto-downloads PDFs/images.​ 🔎 Runs OCR, then uses AI to structure clean invoice fields.​ 📄 Appends parsed data to a Google Sheets “Invoice Database.”​ 🗂️ Uploads the original file to Google Drive with a neat name.​ 💬 Sends a friendly Telegram summary with totals, due date, notes, and link.​ Why it’s useful ⚡ Faster bookkeeping with zero manual copy-paste.​ 🧱 Consistent schema for reliable reporting and pivots.​ 👥 Team-friendly drop-and-log via Telegram.​ 🧩 Easy to extend with approvals, ERP/CRM sync, or vendor routing.​ How it works 📲 Telegram Trigger → file received.​ 🌐 HTTP OCR (OCR.space) → text extracted.​ 🤖 AI Agent → maps to strict JSON schema.​ 📄 Google Sheets → appends structured row.​ 🗂️ Google Drive → saves original invoice.​ 💬 Telegram → concise confirmation and links.​ What you’ll need 🤖 Telegram Bot token.​ 🔑 OCR API key (OCR.space: free tier; upgrade for volume/accuracy).​ 🔐 Google OAuth for Sheets + Drive.​ 🧠 LLM account (e.g., Gemini/OpenAI-compatible).​ Setup steps 🔗 Connect credentials: Telegram, Google, OCR, AI.​ 📄 Prepare Sheet columns: Invoice Number, Date, Total Amount ($), Billing Address, Due Date, Notes.​ 🧭 Update sheet ID and Drive folder ID.​ 🧪 Test: send a sample invoice and validate OCR, AI output, row append, and Drive link.​ Customization ideas 🎯 Higher accuracy OCR: swap to Google Vision.​ 📊 Line items: extract into a second tab for analytics.​ ✅ Approvals: add Telegram keyboard confirmation before write.​ 🧯 Robustness: IF/Retry on empty OCR; user prompt to retake photo.​ Who it’s for 🧑‍💻 Freelancers/agencies needing fast invoice intake via Telegram. 🧾 Small finance teams wanting a searchable ledger with links to originals.​ 🏗️ Builders extending to ERPs/CRMs and custom accounting flows.​ ​ Want help customizing? 📧 anirpoke@gmail.com 🔗 Linkedin

Main Workflow “AI Nextcloud” Entry point**: A public chat-trigger greets the user; every incoming chat message starts the flow. AI agent**: A LangChain agent (“AI Nextcloud”) uses the configured OpenAI model plus short-term memory to continue the dialogue in context. Purpose**: Answers questions about files stored in a Nextcloud folder. The user simply includes the folder path in their question. Tool integration**: Calls the sub-workflow “Nextcloud Tool” whenever it needs to read files and pass their text back to the AI. Sub-Workflow “Nextcloud Tool” Invocation: Triggered by other workflows with the input parameter path (folder path). File listing: Retrieves every file in the specified folder via the Nextcloud API. Filter: Allows only readable formats (PDF, Markdown, DOCX). Download & text extraction PDF → Text via Extract From File Markdown → Raw text DOCX → Text via community node word2text Aggregation: Combines all extracted text into a single output field and returns it. > Outcome: Each call yields the plain content of every supported file in a Nextcloud folder—providing rich context for the AI agent to answer user questions accurately.

This workflow automatically identifies trending topics and hashtags across social media platforms to keep you informed of current trends and viral content. It saves you time by eliminating the need to manually research trending topics and provides data-driven insights for content strategy and social media planning. Overview This workflow automatically scrapes trending hashtag platforms and social media sites to extract currently trending topics, hashtags, and viral content themes. It uses Bright Data to access trend data sources without restrictions and AI to intelligently analyze trending content and provide actionable insights for content creators and marketers. Tools Used n8n**: The automation platform that orchestrates the workflow Bright Data**: For scraping trend platforms and social media without being blocked OpenAI**: AI agent for intelligent trend analysis and content insights Google Sheets**: For storing trending topics data and analysis results 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 trending topics tracking spreadsheet Customize: Define target trend platforms and topics of interest Use Cases Content Marketing**: Discover trending topics for timely and relevant content creation Social Media Strategy**: Plan posts around viral hashtags and trending themes Brand Monitoring**: Track if your brand or industry topics are trending Influencer Marketing**: Identify trending content opportunities for collaborations 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 #trendingtopics #hashtags #brightdata #webscraping #contentmarketing #n8nworkflow #workflow #nocode #socialmediatrends #trendanalysis #viralcontent #contentresearch #socialmediamonitoring #trendtracking #contentdiscovery #hashtagresearch #socialmediamarketing #contentautomation #trendmonitoring #socialmediainsights #contentplanning #trendalerts #viralmarketing #socialtrends #contentoptimization #trendingcontent #socialmediadata #contentintelligence

Description This n8n workflow automates the entire process of creating and publishing AI-generated videos, triggered by a simple message from a Telegram bot (YTAdmin). It transforms a text prompt into a structured video with scenes, visuals, and voiceover, stores assets in MongoDB, renders the final output using Creatomate, and uploads the video to YouTube. Throughout the process, YTAdmin receives real-time updates on the workflow’s progress. This is ideal for content creators, marketers, or businesses looking to scale video production using automation and AI. You can see a video demonstrating this template in action here: https://www.youtube.com/watch?v=EjI-ChpJ4xA&t=200s How it Works Trigger: Message from YTAdmin (Telegram Bot) The flow starts when YTAdmin sends a content prompt. Generate Structured Content A Mistral language model processes the input and outputs structured content, typically broken into scenes. Split & Process Content into Scenes The content is split into categorized parts for scene generation. Generate Media Assets For each scene: Images: Generated using OpenAI’s image model. Voiceovers: Created using OpenAI’s text-to-speech. Audio files are encoded and stored in MongoDB. Scene Composition Assets are grouped into coherent scenes. Render with Creatomate A complete payload is generated and sent to the Creatomate rendering API to produce the video. Progress messages are sent to YTAdmin. The flow pauses briefly to avoid rate limits. Render Callback Once Creatomate completes rendering, it sends a callback to the flow. If the render fails, an error message is sent to YTAdmin. If the render succeeds, the flow proceeds to post-processing. Generate Title & Description A second Mistral prompt generates a compelling title and description for YouTube. Upload to YouTube The rendered video is retrieved from Creatomate. It’s uploaded to YouTube with the AI-generated metadata. Final Update A success message is sent to YTAdmin, confirming upload completion. Set Up Steps (Approx. 10–15 Minutes)Step 1: Set Up YTAdmin Bot Create a Telegram bot via BotFather and get your API token. Add this token in n8n's Telegram credentials and link to the "Receive Message from YTAdmin" trigger. Step 2: Connect Your AI Providers Mistral: Add your API key under HTTP Request or AI Model nodes. OpenAI: Create an account at platform.openai.com and obtain an API key. Use it for both image generation and voiceover synthesis. Step 3: Configure Audio File Storage with MongoDB via Custom API Receives the Base64 encoded audio data sent in the request body. Connects to the configured MongoDB instance (connection details are managed securely within the API- code below). Uses the MongoDB driver and GridFS to store the audio data. Returns the unique _id (ObjectId) of the stored file in GridFS as a response. This _id is crucial as it will be used in subsequent steps to generate the download URL for the audio file. My API code can be found here for reference: https://github.com/nanabrownsnr/YTAutomation.git Step 4: Set Up Creatomate Create a Creatomate account, define your video templates, and retrieve your API key. Configure the HTTP request node to match your Creatomate payload requirements. Step 5: Connect YouTube In n8n, add OAuth2 credentials for your YouTube account. Make sure your Google Cloud project has YouTube Data API enabled. Step 6: Deploy and Test Send a message to YTAdmin and monitor the flow in n8n. Verify that content is generated, media is created, and the final video is rendered and uploaded. Customization Options Change the AI Prompts Modify the generation prompts to adjust tone, voice, or content type (e.g., news recaps, product videos, educational summaries). Switch Messaging Platform Replace Telegram (YTAdmin) with Slack, Discord, or WhatsApp by swapping out the trigger and response nodes. Add Subtitles or Effects Integrate Whisper or another speech-to-text tool to generate subtitles. Add overlay or transition effects in the Creatomate video payload. Use Local File Storage Instead of MongoDB Swap out MongoDB upload http nodes with filesystem or S3-compatible storage. Repurpose for Other Platforms Swap YouTube upload with TikTok, Instagram, or Vimeo endpoints for broader publishing. **Need Help or Want to Customize This Workflow? If you'd like assistance setting this up or adapting it for a different use case, feel free to reach out to me at nanabrownsnr@gmail.com. I'm happy to help!**

This workflow allows you to insert and retrieve data from a table in Stackby. Set node: The Set node is used to set the values for the name and id fields for a new record. You might want to add data from an external source, for example an API or a CRM. Based on your use-case, add the respective node before the Set node and configure your Set node accordingly. Stackby node: This node appends data from the previous node to a table in Stackby. Based on the values you want add to your table, enter the column names in the Column field. Stackby1 node: This node fetches all the data that is stored in the table in Stackby.

An automated n8n workflow that analyzes stocks using RSI and MACD, summarizes insights with OpenAI, and sends a Slack-ready market update every hour. This workflow: Runs hourly from 6:30 AM to 2:30 PM PT, Mon–Fri Checks if the U.S. stock market is open using Alpaca’s /clock API Pulls daily stock bars for a list of tickers via Alpaca’s /v2/stocks/bars Calculates RSI and MACD using a Python code node Categorizes each stock as Buy / Hold / Sell Uses OpenAI Assistant to summarize the results in Slack markdown Sends the message to a specific Slack user or channel

Easily generate images with Black Forest's Flux Text-to-Image AI models using Hugging Face’s Inference API. This template serves a webform where you can enter prompts and select predefined visual styles that are customizable with no-code. The workflow integrates seamlessly with Hugging Face's free tier, and it’s easy to modify for any Text-to-Image model that supports API access. Try it Curious what this template does? Try a public version here: https://devrel.app.n8n.cloud/form/flux Set Up Watch this quick set up video 👇 Accounts required Huggingface.co account (free) Cloudflare.com account (free - used for storage; but can be swapped easily e.g. GDrive) Key Features: Text-to-Image Creation**: Generates unique visuals based on your prompt and style. Hugging Face Integration**: Utilizes Hugging Face’s Inference API for reliable image generation. Customizable Visual Styles**: Select from preset styles or easily add your own. Adaptable**: Swap in any Hugging Face Text-to-Image model that supports API calls. Ideal for: Creators**: Rapidly create visuals for projects. Marketers**: Prototype campaign visuals. Developers**: Test different AI image models effortlessly. How It Works: You submit an image prompt via the webform and select a visual style, which appends style instructions to your prompt. The Hugging Face Inference API then generates and returns the image, which gets hosted on Cloudflare S3. The workflow can be easily adjusted to use other models and styles for complete flexibility.

What problem does this solve? It fetches LinkedIn profiles for a multitude of purposes based on a keyword and location via Google search and stores them in an Excel file for download and in a NocoDB database. It tries to avoid using costly services and should be n8n beginner friendly. It uses the serpapi.com to avoid being blocked by Google Search and to process the data in an easier way. What does it do? Based on criteria input, it searches LinkedIn profiles It discards unnecessary data and turns the follower count into a real number The output is provided as an Excel table for download and in a NocoDB database How does it do it? Based on criteria input, it uses serpAPI.com to conduct Google search of the respective LinkedI profiles With OpenAI.com the name of the respective company is being added With OpenAI.com the follower number e.g., 300+ is turned into a real number: 300 All unnecessary metadata is being discarded As an output an Excel file is being created The output is stored in a nocodb.com table Step-by-step instruction Import the Workflow: Copy the workflow JSON from the "Template Code" section below. Import it into n8n via "Import from File" or "Import from URL". Set up a free account at serpapi.com and get API credentials to enable good Google search results Set up an API account at openai.com and get API key Set up a nocodb.com account (or self-host) and get the API credentials Create the credentials for serpapi.com, opemnai.com and nocodb.com in n8n. Set up a table in NocoDB with the fields indicated in the note above the NocoDB node Follow the instructions as detailed in the notes above individual nodes When the workflow is finished, open the Excel node and click download if you need the Excel file

Description This n8n workflow acts as your personal AI speechwriting coach, directly accessible through Telegram. It listens to your spoken or typed drafts, provides insightful feedback on clarity, engagement, structure, and content, and iteratively refines your message based on your updates. Once you're ready, it synthesizes a brand-new speech or talk incorporating all the improvements and your accumulated ideas. This tool streamlines the speechwriting process, offering on-demand AI assistance to help you craft impactful and well-structured presentations. How it Works Input via Telegram: You interact with the workflow by sending your speech drafts or talking points directly to a designated Telegram bot. AI Feedback: The workflow processes your input using AI models (OpenAI and/or Google Gemini) to analyze various aspects of your speech and provides constructive feedback via Telegram. Iterative Refinement: You can then send updated versions of your speech to the bot, receiving further feedback to guide your revisions. Speech Synthesis: When you send the command to "generate speech," the workflow compiles all your previous input and the AI's feedback to synthesize a new, improved speech or talk, which is then sent back to you via Telegram. New Speech Cycle: By sending the command "new speech," the workflow clears its memory, allowing you to start the process anew for a different topic. Set Up Steps (Takes Approximatly 5 Minutes) Step 1: Create a Telegram Bot and Obtain its ID Open the Telegram application and search for "BotFather". Start a chat with BotFather by clicking "Start" or sending the /start command. Create a new bot by sending the command /newbot. Follow BotFather's instructions to choose a name and username for your bot. Once your bot is created, BotFather will provide you with an API token. Keep this token secure as it's required to connect your n8n workflow to your bot. Step 2: Obtain an OpenAI API Key Go to the OpenAI website (https://platform.openai.com/) and sign up for an account if you don't already have one. Navigate to the API keys section (usually under your profile settings or a "Developers" tab). Click on "Create new secret key". Copy the generated API key and store it securely. You will need to provide this key to your n8n workflow to access OpenAI's language models. Step 3: Obtain a Google Gemini LLM API Key Go to the Google Cloud AI Platform or Google AI Studio website (the specific platform may vary depending on the current Google AI offerings; search for "Google AI API"). Sign up or log in with your Google account. Follow the instructions to enable the Gemini API and create an API key. This might involve creating a project if you haven't already. Copy the generated API key and store it securely. You can then configure your n8n workflow to utilize Google Gemini's language models as well. Customization Options This n8n workflow offers significant flexibility, below are a few options: Modify AI prompts to tailor feedback and generation for presentations, storytelling, interviews, sales pitches, academic talks, and creative writing. Switch the interface from Telegram to Slack, WhatsApp, or even a web interface by replacing the relevant n8n nodes. Integrate analysis for sentiment, keyword density, pacing (with voice input), and filler word detection by adjusting the workflow. Connect to external data sources to provide context to the AI for more targeted feedback and generation. This adaptability allows you to re use this workflow for a wide range of specific use cases and communication environments.

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.