Workflow Templates

This n8n workflow operates as a two-agent system where each agent has a specialized task. The process flows from initial user input to a final analysis, with a seamless handoff between the agents. How it works The Chat Trigger The entire process begins when you send a message using n8n's chat interface. This message serves as the initial prompt or query for the system. The Research Agent Takes Over The user's message is first sent to the Research Agent. This agent's job is to understand the query and gather relevant information. To do this, it has access to: LLM: Google Gemini, which acts as the agent's "brain" to process language and make decisions. Tools: web_search: It uses this tool (powered by your self-hosted SearXNG instance) to perform live searches on the internet. get_current_date: It can access the current date, which is useful for context-aware or time-sensitive research. The Research Agent uses these tools to find the most relevant information related to your query and then compiles it into a concise summary. Handoff to the Sentiment Analysis Agent Once the Research Agent has completed its task, it passes its findings directly to the Sentiment Analysis Agent. The Final Analysis The Sentiment Analysis Agent receives the text from the Research Agent. Its sole purpose, as defined by its system prompt, is to analyze the sentiment of the provided information. It determines if the content is positive, negative, or neutral and formulates a final response. This final analysis is then sent back to you in the chat, completing the workflow. Set up steps Select the Language Model (LLM): This workflow is pre-configured with Google Gemini. You can select a different model for the agents as needed. Configure LLM Credentials: Ensure that valid credentials for your chosen LLM are correctly set up within your n8n instance. Set Up the SearXNG Connection: Configure the node to connect to your self-hosted SearXNG instance. This enables the agent's web search capabilities. Define the Research Agent's Task: Customize the system prompt for the "Research Agent" to define its role, instructions, and how it should conduct its research. Define the Sentiment Analysis Agent's Task: Adjust the system prompt for the "Sentiment Analysis Agent" to specify how it should analyze the information provided by the Research Agent. Test the Workflow: Use the built-in chat interface in the n8n canvas to send a message and verify that the agents are functioning correctly.

WordPress Post Auto-Categorization Workflow 💡 Click here to read detailed case study 📺 Click here to watch youtube tutorial 🎯 Purpose Automatically categorize WordPress blog posts using AI, saving hours of manual work. This workflow analyzes your post titles and assigns them to predefined categories using artificial intelligence. 🔄 What This Workflow Does • Connects to your WordPress site • Retrieves all uncategorized posts • Uses AI to analyze post titles • Automatically assigns appropriate category IDs • Updates posts with new categories • Processes dozens of posts in minutes ⚙️ Setup Requirements WordPress site with admin access Predefined categories in WordPress OpenAI API credentials (or your preferred AI provider) n8n with WordPress credentials 🛠️ Configuration Steps Add your WordPress categories (manually in WordPress) Note down category IDs Update the AI prompt with your category IDs Configure WordPress credentials in n8n Set up AI API connection 🔧 Customization Options • Modify AI prompts for different categorization criteria • Adjust for multiple category assignments • Add tag generation functionality • Customize for different content types • Add additional metadata updates ⚠️ Important Notes • Backup your WordPress database before running • Test with a few posts first • Review AI categorization results initially • Categories must be created manually first 🎁 Bonus Features • Can be modified for tag generation • Works with scheduled posts • Handles bulk processing • Maintains categorization consistency Perfect for content managers, bloggers, and website administrators looking to organize their WordPress content efficiently. #n8n #WordPress #ContentManagement #Automation #AI Created by rumjahn

Purpose The purpose of this automation is to help context switch from office to some side projects or passion gigs so you can be free of distracting thoughts and re-set your perspective. Benefits Anyone who works full time and also does something on the side (perhaps a side gig/being a mom/just follow your passion project) What you need N8N (lol) Any LLM API Key (I used OpenAI 4.1) IPhone (automations and shortcuts) Template Setup Setup LLM API key. Import template file to new workflow. On Iphone create a new shortcut as per video. Create automation steps. Resources Youtube

AI-Agent Code Review for GitHub Pull Requests Description: This n8n workflow automates the process of reviewing code changes in GitHub pull requests using an OpenAI-powered agent. It connects your GitHub repo, extracts modified files, analyzes diffs, and uses an AI agent to generate a code review based on your internal code best practices (fed from a Google Sheet). It ends by posting the review as a comment on the PR and tagging it with a visual label like ✅ Reviewed by AI. 🔧 What It Does Triggered on PR creation Extracts code diffs from the PR Formats and feeds them into an OpenAI prompt Enriches the prompt using a Google Sheet of Swift best practices Posts an AI-generated review as a comment on the PR Applies a PR label to visually mark reviewed PRs ✅ Prerequisites Before deploying this workflow, ensure you have the following: n8n Instance (Self-hosted or Cloud) GitHub Repository with PR activity OpenAI API Key** for GPT-4o, GPT-4-turbo, or GPT-3.5 GitHub OAuth App** (or PAT) connected to n8n to post comments and access PR diffs (Optional) Google Sheets API credentials if using the code best practices lookup node. ⚙️ Setup Instructions 1. Import the Workflow in n8n, click on Workflows → Import from file or JSON Paste or upload the JSON code of this template 2. Configure Triggers and Connections 🔁 GitHub Trigger Node**: PR Trigger Repository**: Select the GitHub repo(s) to monitor Events**: Set to pull_request Auth**: Use GitHub OAuth2 credentials 📥 HTTP Request Node: Get file's Diffs from PR No authentication needed; it uses dynamic path from trigger 🧠 OpenAI Model Node**: OpenAI Chat Model Model**: Select gpt-4o, gpt-4-turbo, or gpt-3.5-turbo Credential**: Provide your OpenAI API Key 🧑‍💻 Code Review Agent Node : Code Review Agent Connected to OpenAI and optionally to tools like Google Sheets 💬 GitHub Comment Poster Uses GitHub API to post review comments back on PR Node: GitHub Robot Credential: Use the agent Github account (OAuth or PAT) Repo : Pick your owen Github Repository 🏷️ PR Labeler (optional) Adds label ReviewedByAI after successful comment Node: Add Label to PR Label : you ca customize the label text of your owen tag. 📊 Google Sheet Best Practices config (optional) Connects to a Google Sheet for coding guideline lookups, we can replace Google sheet by another tool or data base First prepare your best practices list with the clear description and the code bad/good examples Add al the best practices in your Google Sheet Configure* the Code *Best Practices node** in the template : Credential : Use your Google Sheet account by OAuth2 URL : Add your Google Sheet document URL Sheet : Add the name of the best practices sheet

Template for Kids' Story in Arabic The n8n template for creating kids' stories in Arabic offers a versatile platform for storytellers to captivate young audiences with educational and interactive tales. It allows for customization to suit various use cases and can be set up effortlessly. Check this example: https://t.me/st0ries95 Use Cases Educational Platforms: Educational platforms can automate the creation and distribution of educational stories in Arabic for children using this template. By incorporating visual and auditory elements into the storytelling process, educational platforms can enhance learning experiences and engage young learners effectively. Children's Libraries: Children's libraries can utilize this template to curate and share a diverse collection of Arabic stories with young readers. The automated generation of visual content and audio files enhances the storytelling experience, encouraging children to immerse themselves in new worlds and characters through captivating narratives. Language Learning Apps: Language learning apps focused on Arabic can integrate this template to offer culturally rich storytelling experiences for children learning the language. By translating stories into Arabic and supplementing them with visual and auditory components, these apps can facilitate language acquisition in an enjoyable and interactive manner. Configuration Guide for Nodes OpenAI Chat Model Nodes: Functionality**: Allows interaction with the OpenAI GPT-4 Turbo model. Purpose**: Enables communication with advanced chat capabilities. Create a Prompt for DALL-E Node: Customization**: Tailor prompts for generating relevant visual content. Summarization**: Define prompts for visual content generation without text. Generate an Image for the Story Node: Resource Type**: Specifies image as the resource. Prompt Setup**: Configures prompt for textless image creation within the visual content. Generate Audio for the Story Node: Resource Type**: Chooses audio as the resource. Input Definition**: Sets input text for audio file generation. Translate the Story to Arabic Node: Chunking Mode Selection**: Allows advanced chunking mode choice. Summarization Configuration**: Sets method and prompts for story translation into Arabic. Send the Story To Channel Node: Channel ID**: Specifies the channel ID for sending the story text. Text Configuration**: Sets up the text to be sent to the channel. By following these node descriptions, users can effectively configure the n8n template for kids' stories in Arabic, tailoring it to specific use cases for a seamless and engaging storytelling experience for young audiences.

Enhance Query Resolution with the Knowledge Base Tool! Our KB Tool - Confluence KB is crafted to seamlessly integrate into the IT Ops AI SlackBot Workflow, enhancing the IT support process by enabling sophisticated search and response capabilities via Slack. Workflow Functionality: Receive Queries**: Directly accepts user queries from the main workflow, initiating a dynamic search process. AI-Powered Query Transformation**: Utilizes OpenAI's models or local ai to refine user queries into searchable keywords that are most likely to retrieve relevant information from the Knowledge Base. Confluence Integration**: Executes searches within Confluence using the refined keywords to find the most applicable articles and information. Deliver Accurate Responses**: Gathers essential details from the Confluence results, including article titles, links, and summaries, preparing them to be sent back to the parent workflow for final user response. To view a demo video of this workflow in action, click here. Quick Setup Guide: Ensure correct configurations are set for OpenAI and Confluence API integrations. Customize query transformation logic as per your specific Knowledge Base structure to improve search accuracy. Need Help? Dive into our Documentation or get support from the Community Forum! Deploy this tool to provide precise and informative responses, significantly boosting the efficiency and reliability of your IT support workflow.

This n8n workflow shows how using multimodal LLMs with AI vision can tackle tricky image validation tasks which are near impossible to achieve with code and often impractical to be done by humans at scale. You may need image validation when users submitted photos or images are required to meet certain criteria before being accepted. A wine review website may require users only submit photos of wine with labels, a bank may require account holders to submit scanned documents for verification etc. In this demonstration, our scenario will be to analyse a set of portraits to verify if they meet the criteria for valid passport photos according to the UK government website (https://www.gov.uk/photos-for-passports). How it works Our set of portaits are jpg files downloaded from our Google Drive using the Google Drive node. Each image is resized using the Edit Image node to ensure a balance between resolution and processing speed. Using the Basic LLM node, we'll define a "user message" option with the type of binary (data). This will allow us to pass our portrait to the LLM as an input. With our prompt containing the criteria pulled off the passport photo requirements webpage, the LLM is able to validate the photo does or doesn't meet its criteria. A structured output parser is used to structure the LLM's response to a JSON object which has the "is_valid" boolean property. This can be useful to further extend the workflow. Requirements Google Gemini API key Google Drive account Customising this workflow Not using Gemini? n8n's LLM node works with any compatible multimodal LLM so feel free to swap Gemini out for OpenAI's GPT4o or Antrophic's Claude Sonnet. Don't need to validate portraits? Try other use cases such as document classification, security footage analysis, people tagging in photos and more.

How it works Trigger: Listens for an incoming chat message Copy Assistant: Feeds the message (plus memory) into an OpenAI Chat Model and exposes two “tools” Cold Email Writer Tool Sales Letter Tool• Tool execution: Depending on the user’s intent, the appropriate tool generates the copy • Save output: Writes the generated email or sales letter into your target document via the Update a document node Set up steps • Configure your OpenAI Chat Model credentials in n8n (no hard-coded keys!) • Add and authenticate the Simple Memory credential (to keep context across messages) • Create Google Docs (or MS Word) credentials for the Update a document node • Ensure your Chat trigger is pointing at your incoming-message endpoint • Mandatory: Drop sticky-note annotations on each tool node explaining where to enter API keys and how to tweak prompts Once everything’s wired up, send a test chat message like “Write me a cold email for a fintech startup” and watch the workflow spin up a polished draft in your document. How to use Import the workflow JSON into n8n. Configure your Chat trigger (webhook or form) to receive incoming messages. Send a chat prompt like: “Write me a cold email for a B2B SaaS offering.” The “Copy Assistant” custom GPT picks the right tool (Cold Email or Sales Letter). Generated copy is written directly into your linked Google Doc or Word document. Requirements OpenAI API Key (with Chat Completions & Custom GPTs enabled) Custom Assistant created in your ChatGPT dashboard (Assistant ID pasted into the Chat Model node) n8n instance (Cloud or self-hosted) with credentials set up for: Simple Memory (to persist context) Google Docs or Microsoft Word (for document output) Customising this workflow Tweak system and user prompts inside the Copy Assistant node to fit your brand voice. Swap in Slack, Teams or email nodes instead of a document writer to deliver copy where you need it. Add or remove tools (e.g., “Follow-up Email Writer”) by duplicating the existing tool pattern. Use sticky-note annotations on every node to explain where to enter API keys, Assistant IDs, or prompt tweaks.

Idea The idea for app came since I wanted to build a unique gift for my niece because she gets excited for her birthday (which Im going to miss this year). The web app has a simple countdown (in html and JS) but more importantly, there is an AI agent that will answer some specific questions and know her preferences. How it works The questions from app are sent via web hook to N8N which has pulls preferences file (about her likes, dislikes, personality) from postgre and AI Agent that will answer questions/respond. The current status is stored back in postgre (especially about status of cat and universe happenings) before responding back. Features Integrated AI chatbot via N8N webhook Persistent conversation history Minimizable chat interface Fallback support for offline testing Features: -- Wheres Mittens - This is a query to track her lost cat in multiverse. -- Multiverse updates with recent update stored Pre Requisites Postgre SQL database is available. Alternatively, use any other database but change the N8N nodes. LLM Api Key. Step by Step Instructions Export this N8N Workflow. Modify LLM API Key, I used openAI, 4.1 For web app scofflding,you will need Node, HTML and Javascript. I've created a mini version using Node and JS with web app and N8N connection settings here: <https://github.com/productiser/FiBirthdayAgent> PostgreSQL Database Script (1 table for memory and context storage): CREATE TABLE fifi_world_context ( id TEXT PRIMARY KEY, -- e.g., 'agent_fifi' cat_location TEXT, -- e.g., "Bubble Nebula" cat_activity TEXT, -- e.g., "Playing laser tag with moon mice" fifi_preferences JSONB, -- e.g., likes/dislikes/foods/shows world_history TEXT, -- Summary of narrative events last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); 5.Modify system prompt as per your needs. Built With N8N Self hosted Self hosted web app Hosted on Vercel Total spend = <£1 (AI costs only) Total Time = <1 day Support Watch this video for web app overview and how it looks. <https://youtu.be/e7PlrTdvwoM> Contact me on info@pankstr.com/ superllmuser@gmail.com for any queries Hope you enjoy!!

🤖 AI Customer Support Agent with Google Docs Knowledge (Telegram + OpenAI) This no-code workflow turns your Telegram bot into an intelligent, always-on AI support agent that references your business documentation in Google Docs to respond to customer queries—instantly and accurately. Watch full step-by-step video tutorial of the build here: https://youtu.be/Mlv7CjGO7wI 🔧 How it works: Telegram Trigger – Captures incoming messages from users on your Telegram bot Langchain AI Agent (OpenAI GPT) – Interprets the message and uses RAG (retrieval-augmented generation) techniques to craft an answer Google Docs Tool – Connects to and retrieves context from your specified Google Doc (e.g. FAQ, SOPs, policies) Memory Buffer – Keeps track of recent chat history for more human-like conversations Telegram Reply Node – Sends the AI-generated response back to the user 💡 Use Cases: E-commerce customer service SaaS product onboarding Internal helpdesk bot for teams WhatsApp-style support for digital businesses 🧠 What makes this powerful: Supports complex questions by referencing a live Google Doc knowledge base Works in plain conversational language (no buttons or forms needed) Runs 24/7 with zero code Easily extendable to Slack, WhatsApp, or email support 🛠️ Tools used: Telegram Node (trigger + send) Langchain Agent with OpenAI GPT Google Docs Tool Memory Buffer Sticky Notes for easy understanding

Automate Telegram Chat Responses Using Google Gemini By WeblineIndia* ⚡ TL;DR (Quick Steps) Create a Telegram bot using @BotFather and copy the API Token. Obtain Google Gemini API Key via Google Cloud. Set up the n8n workflow: Trigger: Telegram message received. AI Model: Google Gemini generates response. Output: AI reply sent back to user via Telegram. Customize the system prompt, model, or message handling to suit your use case. 🧠 Description This n8n workflow enables seamless automation of real-time chat replies in Telegram by integrating with Google Gemini's Chat Model. Every time a user sends a message to your Telegram bot, the workflow routes it through the Gemini AI, which analyzes and crafts a professional response. This reply is then automatically delivered back to the user. The setup acts as a lightweight but powerful chatbot system — ideal for businesses, customer service, or even personal productivity bots. You can easily modify its tone, intelligence level, or logging mechanisms to cater to specific domains such as sales, tech support, or general Q&A. 🎯 Purpose of the Workflow The primary goal of this workflow is to automate intelligent, context-aware chat responses in Telegram using a robust AI model. It eliminates manual reply handling, enhances user engagement, and ensures 24/7 interaction capabilities — all through a no-code or low-code setup using n8n. 🛠️ Steps to Configure and Use ✅ Pre-Conditions / Requirements Telegram Bot Token**: Get it from @BotFather. Google Gemini API Key**: Available via Google Cloud PaLM/Gemini API access. n8n Instance**: Hosted or local instance with required nodes installed (Telegram, Basic LLM Chain, and Google Gemini support). 🔧 Setup Instructions Step 1: Telegram Trigger – Listen for Incoming Messages Add Telegram Trigger node. Select Trigger On: Message. Authenticate using your Telegram Bot Token. This will capture incoming messages from any user interacting with your bot. Step 2: Google Gemini AI – Generate a Smart Reply Add the Basic LLM Chain node. Connect the input message ({{$json.message.text}}) from the Telegram Trigger. System Prompt: > "You are an AI assistant. Reply to the following user message professionally:" Choose Google Gemini Chat Model (models/gemini-1.5-pro). Connect this node to receive the text input and pass it to Gemini for processing. Step 3: Telegram Reply – Send the AI Response Add a Telegram node (Operation: Send Message). Set Chat ID dynamically from the Telegram Trigger node. Input the generated message from the Gemini output. Enable Parse Mode as HTML for rich formatting. Final Step: Link All Nodes Receive Telegram Message → Generate AI Response → Send Telegram Reply. > Tip: Test the workflow by sending a message to your Telegram bot and ensure you receive an AI-generated reply. 🧩 Customization Guidance ✏️ Modify the AI tone by updating the system prompt. 🤖 Use other AI models (e.g., OpenAI GPT-4o). 🔍 Add filters to respond differently based on specific keywords. 📊 Extend the workflow to store chats in Google Sheets, Airtable, or databases for audit or analytics. 🌐 Multi-language support: Add translation layers before and after AI processing. 🛠️ Troubleshooting Guide No message received?** Check if your Telegram bot is active and webhook is working. AI not responding?** Validate your Google Gemini API key and usage quota. Wrong replies?** Refine the system prompt or validate message routing. Formatting issues?** Ensure Parse Mode is correctly set to HTML. 💡 Use Case Examples Customer Service Chatbot** for product queries. Educational Bots** for answering user questions on a topic. Mental Health Companion** that gives supportive replies. Event-based Announcers** or automatic responders during off-hours. > And many more! This workflow can be easily extended to support advanced use cases with just a few additional nodes. 👨‍💻 About the Creator This workflow is developed by WeblineIndia, a trusted provider of AI development services and process automation solutions. If you're looking to build or customize intelligent workflows like this, we invite you to get in touch with our team. We also offer specialized Python development and AI developer hiring services to supercharge your automation needs.

This workflow template, "Daily Gratitude Reminder Bot for LINE," is designed to help users cultivate a habit of gratitude by sending personalized, AI-generated reminders every evening at 9:00 PM. Using Azure OpenAI , the bot generates varied and engaging messages to prompt users to reflect on the positive aspects of their day. The reminders are then sent directly to users via the LINE messaging platform , ensuring a seamless and impactful experience. Whether you're a developer, counselor, or business owner, this template offers a customizable and scalable solution for promoting mental wellness and fostering a culture of gratitude. profile Qwen2.5-Max 2:56 pm Here’s a polished and detailed description for your "Daily Gratitude Reminder Bot for LINE" template, following the submission guidelines provided. This description is structured to meet all the requirements, including SEO optimization, clarity, and user convenience. Daily Gratitude Reminder Bot for LINE This workflow template, "Daily Gratitude Reminder Bot for LINE," is designed to help users cultivate a habit of gratitude by sending personalized, AI-generated reminders every evening at 9:00 PM. Using Azure OpenAI , the bot generates varied and engaging messages to prompt users to reflect on the positive aspects of their day. The reminders are then sent directly to users via the LINE messaging platform , ensuring a seamless and impactful experience. Whether you're a developer, counselor, or business owner, this template offers a customizable and scalable solution for promoting mental wellness and fostering a culture of gratitude. Who Is This Template For? Developers who want to integrate AI-powered workflows into messaging platforms like LINE. Counselors & Therapists looking to encourage mindfulness and emotional well-being among their clients. Businesses & Organizations focused on employee wellness or customer engagement through positive reinforcement. Educators & Nonprofits seeking tools to promote mental health awareness and self-care practices. What Problem Does This Workflow Solve? Gratitude journaling has been proven to improve mental health, reduce stress, and increase overall happiness. However, many people struggle to maintain the habit due to busy schedules or forgetfulness. This workflow solves that problem by automating daily reminders to reflect on positive experiences, making it easier for users to build and sustain a gratitude practice. What This Workflow Does Scheduled Trigger: The workflow is triggered every evening at 9:00 PM using a schedule node. AI-Powered Message Generation: An Azure OpenAI Chat Model generates a unique and engaging reminder message with a temperature setting of 0.9 to ensure variety and creativity. Message Formatting: The generated message is reformatted to comply with the LINE Push API requirements, ensuring smooth delivery. Push Notification via LINE: The formatted message is sent to the user via the LINE Push API , delivering the reminder directly to their chat. Setup Guide Pre-Requisites Access to an Azure OpenAI account with credentials. A LINE Developers Console account with access to the Push API. Basic knowledge of n8n workflows and JSON formatting. How to Customize This Workflow to Your Needs Change the Time: Adjust the schedule trigger to send reminders at a different time. Modify the Prompt: Edit the AI model's input prompt to generate messages tailored to your audience (e.g., focus on work achievements or personal growth). Expand Recipients: Update the LINE Push API node to send reminders to multiple users or groups. Integrate Additional Features: Add nodes to log user responses or track engagement metrics. Why Use This Template? Promotes Mental Wellness: Encourages users to reflect on positive experiences, improving emotional well-being. Highly Customizable: Easily adapt the workflow to suit different audiences and use cases. Scalable: Send reminders to one user or thousands, making it suitable for both personal and organizational use. AI-Powered Creativity: Avoid repetitive messages by leveraging AI to generate fresh and engaging content.