Workflow Templates

This n8n template helps you build a full AI-powered LinkedIn content generator with just a few clicks. Paired with the free WeWeb UI template, it becomes a ready-to-use web app where users can: Add their own OpenAI API key Customize the prompt and define 6 content topics Edit the AI-generated topics Choose when to generate LinkedIn posts, complete with hashtags and an optional image Who This Is For Perfect for marketers, indie hackers, and solopreneurs who want to build their personal brand on LinkedIn while staying in control of what gets posted. 🧠 What Makes This Different Unlike most AI agents, you stay fully in control: You define the tone and focus via the prompt. You choose which topics to keep or modify. You decide when to generate a post. You can build on top of this and create your own SaaS product. It’s also modular and extendable—hook it up to your backend, add user login, or feed AI improvements based on user input. ⚙️ How It Works Triggering Events: The app includes 3 pre-configured triggers, ready to be hooked into your WeWeb frontend. Just update the webhook URLs after duplicating the n8n workflow. Topic Generation: A call is made to OpenAI (GPT-4) to generate topic ideas based on your prompt. Post Creation: Once topics are approved or edited, GPT-4 writes full posts with suggested hashtags. Image Generation (Optional): If enabled, a DALL·E call generates a relevant image. Everything Stays Local: All data and images are handled locally, no cloud storage setup needed. 🧪 Requirements & Setup No fancy infrastructure required. Here’s what helps you get started: Free WeWeb account** (recommended) to use the frontend UI template OpenAI account** with API access (for GPT-4 and DALL·E) n8n account** (self-hosted or cloud) to run the backend workflow The template is completely free to use. Since each user adds their own OpenAI API key, you don't need to worry about usage costs or rate limits on your end. 🔧 Want to Go Further? This setup is beginner-friendly, but developers can: Add user accounts Save post history Feed user feedback back into the prompt logic Launch their own branded version as a SaaS

How it works This template is a complete, hands-on tutorial that lets you build and interact with your very first AI Agent. Think of an AI Agent as a standard AI chatbot with superpowers. The agent doesn't just talk; it can use tools to perform actions and find information in real-time. This workflow is designed to show you exactly how that works. The Chat Interface (Chat Trigger): This is your window to the agent. It's a fully styled, public-facing chat window where you can have a conversation. The Brain (AI Agent Node): This is the core of the operation. It takes your message, understands your intent, and intelligently decides which "superpower" (or tool) it needs to use to answer your request. The agent's personality and instructions are defined in its extensive system prompt. The Tools (Tool Nodes): These are the agent's superpowers. We've included a variety of useful and fun tools to showcase its capabilities: Get a random joke. Search Wikipedia for a summary of any topic. Calculate a future date. Generate a secure password. Calculate a monthly loan payment. Fetch the latest articles from the n8n blog. The Memory (Memory Node): This gives the agent a short-term memory, allowing it to remember the last few messages in your conversation for better context. When you send a message, the agent's brain analyzes it, picks the right tool for the job, executes it, and then formulates a helpful response based on the tool's output. Set up steps Setup time: ~3 minutes This template is nearly ready to go out of the box. You just need to provide the AI's "brain." Configure Credentials: This workflow requires an API key for an AI model. Make sure you have credentials set up in your n8n instance for either Google AI (Gemini) or OpenAI. Choose Your AI Brain (LLM): By default, the workflow uses the Google Gemini node. If you have Google AI credentials, you're all set! If you prefer to use OpenAI, simply disable the Gemini node and enable the OpenAI node. You only need one active LLM node. Make sure it is connected to the Agent parent node. Explore the Tools: Take a moment to look at the different tool nodes connected to the Your First AI Agent node. This is where the agent gets its abilities! You can add, remove, or modify these to create your own custom agent. Activate and Test! Activate the workflow. Open the public URL for the Example Chat Window node (you can copy it from the node's panel). Start chatting! Try asking it things like: "Tell me a joke." "What is n8n?" "Generate a 16-character password for me." "What are the latest posts on the n8n blog?" "What is the monthly payment for a $300,000 loan at 5% interest over 30 years?"

This n8n template demonstrates one approach to achieve a more natural and less frustration conversations with AI agents by reducing interrupts by predicting the end of user utterances. When we text or chat casually, it's not uncommon to break our sentences over multiple messages or when it comes to voice, break our speech with the odd pause or umms and ahhs. If an agent replies to every message, it's likely to interrupt us before we finish our thoughts and it can get very annoying! Previously, I demonstrated a simple technique for buffering each incoming message by 5 seconds but that approach still suffers in some scenarios when more time is needed. This technique has no arbitrary time limit and instead uses AI to figure out when its the agent's turn based on the user's message, allowing for the user to take all the time they need. How it works Telegram messages are received but no reply is generated for them by default. Instead they are sent to the prediction subworkflow to determine if a reply should be generated. The prediction subworkflow begins by checking Redis for the current user's prediction session state. If this is a new "utterance", it kicks off the "predict end of utterance" loop - the purpose of which is to buffer messages in a smart way! New users message can continue to be accepted by the workflow until enough is collected to allow our prediction classifier to determine the end of the utterance has been reached. The loop is then broken and the buffered chat messages are combined and sent to the AI agent to generate a response and sent to the user via the telegram node. The prediction session state is then deleted to signal the workflow is ready to start again with a new message. How to use This system sits between your preferred chat platform and the AI agent so all you need to do is replace the telegram nodes as required. Where LLM-only prediction isn't working well enough, consider more traditional code-based checking of heuristics to improve the detection. Ideally you'll want a fast but accurate LLM so your user isn't waiting longer than they have to - at time of writing Gemini-2.5-flash-lite was the fastest in testing but keep a look out for smaller and more powerful LLMs in the future. Requirements Gemini for LLM Redis for session management Telegram for chat platform

Learn how to build an MCP Server and Client in n8n with official nodes. > ⚠ Requires n8n version 1.88.0 or higher. In this example, we use Google Calendar and custom functions as two separate MCP Servers, demonstrating how to integrate both native and custom tools. How it works The AI Agent connects to two MCP Servers. Each MCP Trigger (Server) generates a URL exposing its tools. This URL is used by an MCP Client linked to the AI Agent. Whenever you make changes to the tools, there’s no need to modify the MCP Client. It automatically keeps the AI Agent informed on how to use each tool, even if you change them over time. That’s the power of MCP 🙌 Who is this template for Anyone looking to use MCP with their AI Agents. How to set up Instructions are included within the workflow itself. Check out my other templates 👉 https://n8n.io/creators/solomon/

Transform your meetings into actionable insights automatically! This workflow captures meeting audio, transcribes conversations, generates AI summaries, and emails the results to participants—all without manual intervention. What's the Goal? Auto-record meetings** when they start and stop when they end Transcribe audio** to text using Vexa Bot integration Generate intelligent summaries** with AI-powered analysis Email summaries** to meeting participants automatically Eliminate manual note-taking** and post-meeting admin work Never miss important discussions** or action items again Why Does It Matter? Save 90% of Post-Meeting Time**: No more manual transcription or summary writing Never Lose Key Information**: Automatic capture ensures nothing falls through cracks Improve Team Productivity**: Focus on discussions, not note-taking Perfect Meeting Records**: Searchable transcripts and summaries for future reference Instant Distribution**: Summaries reach all participants immediately after meetings How It Works Step 1: Meeting Detection & Recording Start Meeting Trigger**: Detects when meeting begins via Google Meet webhook Launch Vexa Bot**: Automatically joins meeting and starts recording End Meeting Trigger**: Detects meeting end and stops recording Step 2: Audio Processing & Transcription Stop Vexa Bot**: Ends recording and retrieves audio file Fetch Meeting Audio**: Downloads recorded audio from Vexa Bot Transcribe Audio**: Converts speech to text using AI transcription Step 3: AI Summary Generation Prepare Transcript**: Formats transcribed text for AI processing Generate Summary**: AI model creates concise meeting summary with: Key discussion points Decisions made Action items assigned Next steps identified Step 4: Distribution Send Email**: Automatically emails summary to all meeting participants Setup Requirements Google Meet Integration: Configure Google Meet webhook and API credentials Set up meeting detection triggers Test with sample meeting Vexa Bot Configuration: Add Vexa Bot API credentials for recording Configure audio file retrieval settings Set recording quality and format preferences AI Model Setup: Configure AI transcription service (e.g., OpenAI Whisper, Google Speech-to-Text) Set up AI summary generation with custom prompts Define summary format and length preferences Email Configuration: Set up SMTP credentials for email distribution Create email templates for meeting summaries Configure participant list extraction from meeting metadata Import Instructions Get Workflow JSON: Copy the workflow JSON code Open n8n Editor: Navigate to your n8n dashboard Import Workflow: Click menu (⋯) → "Import from Clipboard" → Paste JSON → Import Configure Credentials: Add API keys for Google Meet, Vexa Bot, AI services, and SMTP Test Workflow: Run a test meeting to verify end-to-end functionality Your meetings will now automatically transform into actionable summaries delivered to your inbox!

How it works This workflow adds a priority to each Todoist item in your inbox, based on a list of projects that you add in the workflow. Setup Add your Todoist credentials Add your OpenAI credentials Set your project names and add priority

How it works • Transcribes a WhatsApp voice or text message from a prospect using Whisper or GPT • Extracts key information (name, need, context, urgency) via AI • Matches the most relevant service pack by comparing the prospect’s need with Airtable data • Dynamically fills a branded template via APITEMPLATE (HTML or PDF) • Generates a clean, personalized business proposal — including dynamic links (payment, calendar, etc.) • Sends the final PDF back instantly via WhatsApp or email Set up steps • ⏱ Estimated setup time: 45–60 minutes • ✅ You’ll need:  ◦ WhatsApp Business Cloud API access (with webhook configured)  ◦ OpenAI API key (Whisper + GPT)  ◦ Airtable (to store service packs and client input)  ◦ APITEMPLATE account (template with placeholders like {{nom}}, {{prix}}, {{lien_reservation}}, etc.)  ◦ n8n instance (cloud or self-hosted) • 📦 Create your service packs in Airtable with associated links (Stripe, Calendly…) • 🔗 The proposal auto-includes these links dynamically inside the PDF • 🚀 Workflow orchestrates the end-to-end process: from WhatsApp input to PDF delivery

Create a recommendation tool without hallucinations based on RAG with the Qdrant Vector database. This example is based on movie recommendations on the IMDB-top1000 dataset. You can provide your wishes and your "big no's" to the chatbot, for example: "A movie about wizards but not Harry Potter", and get top-3 recommendations. How it works a video with the full design process Upload IMDB-1000 dataset to Qdrant Vector Store, embedding movie descriptions with OpenAI; Set up an AI agent with a chat. This agent will call a workflow tool to get movie recommendations based on a request written in the chat; Create a workflow which calls Qdrant's Recommendation API to retrieve top-3 recommendations of movies based on your positive and negative examples. Set Up Steps You'll need to create a free tier Qdrant Cluster (Qdrant can also be used locally; it's open-sourced) and set up API credentials You'll OpenAI credentials You'll need GitHub credentials & to upload the IMDB Kaggle dataset to your GitHub.

Who’s it for 👥 This template is perfect for content creators, marketers, and researchers managing WeChat public account articles! 🚀 It’s ideal for n8n newcomers or anyone wanting to save time on manual content analysis, especially if you use Google Sheets for tracking. 📊 Whether you’re into AI, 欧阳良宜, or automation, this is for you! 😄 How it works / What it does 🔧 This workflow automates the retrieval, filtering, classification, and summarization of WeChat articles. 🌐 It reads RSS feed links from a Google Sheet, filters articles from the last 10 days ⏳, cleans HTML content 🧹, classifies them as relevant or not 🎯, generates insightful Chinese summaries with AI 🤖, and saves results to Google Sheets and Notion. 📝 Outputs are Slack-formatted for team collaboration! 💬 How to set up 🛠️ Prepare Google Sheets: Use your own documentId (replace the example) and set up sheets "Save Initial Links" (gid=198451233) and "Save Processed Data" (gid=1936091950). 📋 Configure Credentials: Add Google Sheets and OpenAI API credentials—avoid hardcoding keys! 🔐 Set RSS Feed: Update the rss_feed_url in the "RSS Read" node with your WeChat RSS feed. 🌐 Customize AI: Tweak "Relevance Classification" and "Basic LLM Chain" prompts for your topics (e.g., 欧阳良宜, AI). 🎨 Notion (Optional): Swap the databaseId (e.g., 22e79d55-2675-8055-a143-d55302c3c1b1) with your own. 📚 Run Workflow: Trigger manually via the "When clicking ‘Execute workflow’" node. 🚀 Requirements ✅ n8n account with Google Sheets and OpenAI integrations. Access to a WeChat public account RSS feed. Basic JSON and node config knowledge. How to customize the workflow 🎛️ Topic Adjustment: Update categories in "Relevance Classification" for new topics (e.g., "technology", "education"). 🌱 Summary Length: Modify the LLM prompt in "Basic LLM Chain" to adjust length or style. ✂️ Output Destination: Add Slack or Email nodes for more outputs. 📩 Date Filter: Change the "IF (Filter by Date)" condition (e.g., 7 days instead of 10). ⏰ Scalability: Use a "Schedule Trigger" node for automation. ⏳

This n8n template provides a powerful AI-powered chatbot that acts as your personal Spotify DJ. Simply tell the chatbot what kind of music you're in the mood for, and it will intelligently create a custom playlist, give it a fitting name, and populate it with relevant tracks directly in your Spotify account. The workflow is built to be flexible, allowing you to easily change the underlying AI model to your preferred provider, making it a versatile starting point for any AI-driven project. Who is this for? Music lovers:** Instantly create playlists for any activity, mood, or genre without interrupting your flow. Developers & AI enthusiasts:** A perfect starting point to understand how to build a functional AI Agent that uses tools to interact with external services. Automation experts:** See a practical example of how to chain AI actions and sub-workflows for more complex, stateful automations. What problem does this solve? Manually creating a good playlist is time-consuming. You have to think of a name, search for individual songs, and add them one by one. This workflow solves that by: Automating playlist creation:** Turns a simple natural language request (e.g., "I need a playlist for my morning run") into a fully-formed Spotify playlist. Reducing manual effort:** Eliminates the tedious task of searching for and adding multiple tracks. Providing player control:** Allows you to manage your Spotify player (play, pause, next) directly from the chat interface. Centralizing music management:** Acts as a single point of control for both creating playlists and managing playback. How it works Trigger & input: The workflow starts when you send a message in the Chat Trigger interface. AI agent & tool-use: An AI Agent, powered by a Large Language Model (LLM), interprets your message. It has access to a set of "tools" that allow it to interact with Spotify. Playlist creation sub-workflow: If you ask for a new playlist, the Agent calls a sub-workflow using the Create new playlist tool. This sub-workflow uses another AI call to brainstorm a creative playlist name and a list of suitable songs based on your request. Spotify actions: The sub-workflow then connects to Spotify to: Create a new, empty playlist with the generated name. Search for each song from the AI's list to get its official Spotify Track ID. Add each track to the new playlist. Player control: If your request is to control the music (e.g., "pause the music"), the Agent uses the appropriate tool (Pause player, Resume player, etc.) to directly control your active Spotify player. Setup Accounts & API keys: You will need active accounts and credentials for: Your AI provider (e.g., OpenAI, Groq, local LLMs via Ollama): To power the AI Agent and the playlist generation. Spotify: To create playlists and control the player. You'll need to register an application in the Spotify Developer Dashboard to get your credentials. Configure credentials: Add your AI provider's API key to the Chat Model nodes. The template uses OpenAI by default, but you can easily swap this out for any compatible Langchain model node. Add your Spotify OAuth2 credentials to all Spotify and Spotify Tool nodes. Activate workflow: Once all credentials are set and the workflow is saved, click the "Active" toggle. You can now start interacting with your Spotify AI Agent via the chat panel! Taking it further This template is a great foundation. Here are a few ideas to expand its capabilities: Become the party DJ:** Make the Chat Trigger's webhook public. You can then generate a QR code that links to the chat URL. Party guests can scan the code and request songs directly from their phones, which the agent can add to a collaborative playlist or the queue. Expand the agent's skills:** The Spotify Tool node has more actions available. Add a new tool for Add to Queue so you can ask the agent to queue up a specific song without creating a whole new playlist. Integrate with other platforms:** Swap the Chat Trigger for a Telegram or Discord trigger to build a Spotify bot for your community. You could also connect it to a Webhook to take requests from a custom web form.

Who is this for? This workflow is ideal for: HR teams and recruiters seeking to streamline resume screening. Hiring managers who want quick, summarized candidate insights. Recruitment agencies handling large volumes of applicant data. Startups and small businesses looking to automate hiring without complex systems. AI and automation professionals who want to build smart HR workflows using n8n and OpenAI. What problem is this workflow solving? / Use Case Manually reviewing resumes is time-consuming, inconsistent, and prone to human bias. This workflow automates the resume intake and evaluation process—ensuring that each applicant is screened, summarized, and scored using a consistent, data-driven method. It enhances efficiency and supports better hiring decisions. What this workflow does Accepts resume submissions via form and saves files to Google Drive. Extracts key information from resumes using AI (e.g., name, contact, education, experience). Summarizes candidate qualifications into a short, readable profile. Allows HR to rate applicants and leave comments. Logs all extracted data and evaluations into a centralized Google Sheet for tracking. Setup Resume is submitted through an n8n form. The uploaded file is automatically stored in Google Drive. n8n uses OpenAI and document parsing tools to extract candidate data. Extracted information is structured and summarized using GPT. A review form is triggered for internal HR rating and notes. All data is appended to a Google Sheet for records and filtering. How to customize this workflow to your needs** Change the form tool (e.g., Typeform, Tally, or custom HTML) based on your stack. Adapt the summary prompt to align with your specific role requirements. Add filters to auto-flag top-tier candidates based on score or skills. Integrate Slack or email to notify hiring managers when top resumes are processed. Connect to your ATS if you want to push processed resumes into your recruitment system.

Automated Financial Tracker: Telegram Invoices to Notion with AI Summaries & Reports Tired of manually logging every expense? Streamline your financial tracking with this powerful n8n workflow! Snap a photo of your invoice in Telegram, and let AI (powered by Google Gemini) automatically extract the details, record them in your Notion database, and even send you a quick summary. Plus, get scheduled weekly reports with charts to visualize your spending. Automate your finances, save time, and gain better insights with this easy-to-use template! Transform your expense tracking from a chore into an automated breeze. Try it out! Overview: This workflow revolutionizes how you track your finances by automating the entire process from invoice capture to reporting. Simply send a photo of an invoice or receipt to a designated Telegram chat, and this workflow will: Extract Data with AI: Utilize Google Gemini's capabilities to perform OCR on the image, understand the content, and extract key details like item name, quantity, price, total, date, and even attempt to categorize the expense. Store in Notion: Automatically log each extracted transaction into a structured Notion database. Instant Feedback: Send a summary of the processed transaction back to your Telegram chat. Scheduled Reporting: Generate and send a visual summary of your expenses (e.g., weekly spending by category) as a chart to your preferred Telegram chat or group. This workflow is perfect for individuals, freelancers, or small teams looking to effortlessly manage their expenses without manual data entry. Key Features & Benefits: Effortless Expense Logging:** Just send a picture – no more typing! AI-Powered Data Extraction:** Leverages Google Gemini for intelligent invoice processing. Centralized Data in Notion:** Keep all your financial records neatly organized in a Notion database. Automated Categorization:** AI helps in categorizing your expenses (e.g., Food & Beverage, Transportation). Instant Summaries:** Get immediate confirmation and a summary of what was recorded. Visual Reporting:** Receive scheduled charts (e.g., bar charts of spending by category) directly in Telegram. Customizable:** Easily adapt the workflow to your specific needs, categories, and reporting preferences. Time-Saving:** Drastically reduces the time spent on manual financial administration. How It Works (Workflow Breakdown): The workflow is divided into two main parts: Part 1: Real-time Invoice Processing & Logging (## Auto Notes Transaction with Telegram and Notion database) Telegram Trigger (Telegram Trigger | When recive photo): Activates when a new photo is sent to the configured Telegram chat. Get Photo Info (Get Info Photo from telegram chat): Retrieves the details of the received photo. Get Image Info (Get Image Info): Prepares the image data. AI Data Extraction (Google Gemini Chat Model & Basic LLM Chain): The image data is sent to the Google Gemini Chat Model. A specific prompt instructs the AI to extract details (date, ID, name, quantity, price, total, category, tax) in a JSON array format and provide a summary message. The categories include Food & Beverage, Transportation, Utilities, Shopping, Healthcare, Entertainment, Housing, and Education. Parse AI Output (Parse To your object | Table): Structures the AI's JSON output for easier handling. Split Transactions (Split Out | data transaction): If an invoice contains multiple items, this node splits them into individual records. Record to Notion (Record To Notion Database): Each transaction item is added as a new page/entry in your specified Notion database, mapping fields like Name, Quantity, Price, Total, Category, Date, and Tax. Send Telegram Summary (Sendback to chat and give summarize text): The summary message generated by the AI is sent back to the original Telegram chat. Part 2: Scheduled Financial Reporting (## Schedule report to send on chanel or private message) Schedule Trigger (Schedule Trigger | for send chart report): Runs at a predefined interval (e.g., every week) to generate reports. Get Recent Data from Notion (Get Recent Data from Notions): Fetches transaction data from the Notion database for a specific period (e.g., the past week). Summarize Data (Summarize Transaction Data): Aggregates the data, for example, by summing up the 'total' amount for each 'category'. Prepare Chart Data (Convert Data to JSON chart payload): Transforms the summarized data into a JSON format suitable for generating a chart (e.g., labels for categories, data for spending amounts). Generate Chart (Generate Chart): Uses the QuickChart node to create a visual chart (e.g., a bar chart) from the prepared data. Send Chart to Telegram (Send Chart Image to Group or Private Chat): Sends the generated chart image to a specified Telegram chat ID or group. Nodes Used (Key Nodes): Telegram Trigger & Telegram Node:** For receiving images and sending messages/images. Google Gemini Chat Model (Langchain):** For AI-powered OCR and data extraction from invoices. Basic LLM Chain (Langchain):** To interact with the language model using specific prompts. Output Parser Structured (Langchain):** To structure the output from the language model. Notion Node:** For reading from and writing to your Notion databases. Schedule Trigger:** To automate the reporting process. Summarize Node:** To aggregate data for reports. Code Node:** Used here to format data for the chart. QuickChart Node:** For generating charts. SplitOut Node:** To process multiple items from a single invoice. Setup Instructions: Credentials: Telegram: Create a Telegram bot and get its API token. You'll also need the Chat ID where you'll send invoices and where reports should be sent. Google Gemini (PaLM) API: You'll need an API key for Google Gemini. Notion: Create a Notion integration and get the API key. Create a Notion database with properties corresponding to the data you want to save (e.g., Name (Title), Quantity (Number), Price (Number), Total (Number), Category (Select), Date (Text or Date), Tax (Number)). Share this database with your Notion integration. Configure Telegram Trigger: Add your Telegram Bot API token. When you first activate the workflow or test the trigger, send /start to your bot in the chat you want to use for sending invoices. n8n will then capture the Chat ID. Configure Google Gemini Node (Google Gemini Chat Model): Select or add your Google Gemini API credentials. Review the prompt in the Basic LLM Chain node and adjust if necessary (e.g., date format, categories). Configure Notion Nodes: Record To Notion Database: Select or add your Notion API credentials. Select your target Notion Database ID. Map the properties from the workflow (e.g., ={{ $json.name }}) to your Notion database columns. Get Recent Data from Notions: Select or add your Notion API credentials. Select your target Notion Database ID. Adjust the filter if needed (default is "past_week"). Configure Telegram Node for Reports (Send Chart Image to Group or Private Chat): Select or add your Telegram Bot API token. Enter the Chat ID for the group or private chat where you want to receive the reports. Configure Schedule Trigger (Schedule Trigger | for send chart report): Set your desired schedule (e.g., every Monday at 9 AM). Test: Send an image of an invoice to your Telegram bot and check if the data appears in Notion and if you receive a summary message. Wait for the scheduled report or manually trigger it to test the reporting functionality. Sticky Note Text for Your n8n Template: (These are suggestions. You would place these directly into the sticky notes within your n8n workflow editor.) Existing High-Level Sticky Notes: ## Auto Notes Transaction with Telegram and Notion database ## Schedule report to send on chanel or private message Specific Sticky Notes to Add: On Telegram Trigger | When recive photo:** 📸 INVOICE INPUT 📸 Bot listens here for photos of your receipts/invoices. Ensure your Telegram Bot API token is set in credentials. Near Google Gemini Chat Model & Basic LLM Chain:** 🤖 AI MAGIC HAPPENS HERE 🧠 Image is sent to Google Gemini for data extraction. Check 'Basic LLM Chain' to customize the AI prompt (e.g., categories, output format). Requires Google Gemini API credentials. On Parse To your object | Table:** ✨ STRUCTURING AI DATA ✨ Converts the AI's text output into a usable JSON object. Check the schema if you modify the AI prompt significantly. On Record To Notion Database:** 📝 SAVING TO NOTION 📝 Extracted transaction data is saved here. Configure with your Notion API key & Database ID. Map fields correctly to your database columns! On Sendback to chat and give summarize text:** 💬 TRANSACTION SUMMARY 💬 Sends a confirmation message back to the user in Telegram with a summary of the recorded expense. On Schedule Trigger | for send chart report:** 🗓️ REPORTING SCHEDULE 🗓️ Set how often you want to receive your spending report (e.g., weekly, monthly). On Get Recent Data from Notions:** 📊 FETCHING DATA FOR REPORT 📊 Retrieves transactions from Notion for the report period. Default: "Past Week". Adjust filter as needed. Requires Notion API credentials & Database ID. On Summarize Transaction Data:** ➕ SUMMARIZING SPENDING ➕ Aggregates your expenses, usually by category, to prepare for the chart. On Convert Data to JSON chart payload (Code Node):** 🎨 PREPARING CHART DATA 🎨 This Code node formats the summarized data into the JSON structure needed by QuickChart. On Generate Chart (QuickChart Node):** 📈 GENERATING VISUAL REPORT 📈 Creates the actual chart image based on your spending data. You can customize chart type (bar, pie, etc.) here. On Send Chart Image to Group or Private Chat:** 📤 SENDING REPORT TO TELEGRAM 📤 Delivers the generated chart to your chosen Telegram chat/group. Set the correct Chat ID and Bot API token. General Sticky Note (Place where relevant):** 🔑 CREDENTIALS NEEDED 🔑 Remember to set up API keys/tokens for: Telegram Google Gemini Notion General Sticky Note (Place where relevant):** 💡 CUSTOMIZE ME! 💡 Adjust AI prompts for better accuracy. Change Notion database structure. Modify report frequency and content. `