by Nikhil Kuriakose
How it works Triggers on submitting an n8n form Uses the form details to prepare a message Sends the message to Slack Set up Steps Add in your team name Add in message tone Set up Open AI Set up Slack
by Hendriekus
Find OAuth URIs with AI Llama Overview: The AI agent identifies: Authorization URI Token URI Audience Methodology: Confidence scoring is utilized to assess the trustworthiness of extracted data: Score Range: 0 < x ≤ 1 Score Granularity: 0.01 increments Model Details: Leveraging the Wayfarer Large 70b Llama 3.3 model. How it works: This template is designed to assist users in obtaining OAuth2 settings using AI-powered insights. It is ideal for developers, IT professionals, or anyone working with APIs that require OAuth2 authentication. By leveraging the AI agent, users can simplify the process of extracting and validating key details such as the authorization_url, token_url, and audience. Set up instructions: 1. Configuration Nodes Structured Output Node**: Parses the AI model's output using a predefined JSON schema. This ensures the data is structured for downstream processing. Code Node**: If the AI model’s output does not match the required format, use the Code node to re-arrange and transform the data. Example code snippets are provided below for common scenarios. 2. AI Model Prompt The prompt for the AI model includes: A detailed structure and objectives of the query. Flexibility for the model to improvise when accurate results cannot be determined. 3. Confidence Scoring The AI model assigns a confidence score (0 < x ≤ 1) to indicate the reliability of the extracted data. Scores are provided in increments of 0.01 for granularity. Adaptability Customize this template: Update the AI model prompt with details specific to your API or OAuth2 setup. Adjust the JSON schema in the Structured Output node to match the data format. Modify the Code logic to suit the application's requirements.
by Oneclick AI Squad
In this guide, we’ll walk you through setting up an AI-driven workflow that automatically processes highly-rated food photos from a Google Sheet, generates AI-powered captions, shares them to Pinterest, and updates the sheet to reflect the posts. Ready to automate your food photo sharing? Let’s dive in! What’s the Goal? Automatically detect and process highly-rated food photos (4 stars or above) from a Google Sheet. Use AI to generate engaging and relevant captions. Share the photos with captions to Pinterest via the Pinterest API. Update the Google Sheet to mark photos as posted. Enable scheduled automation for consistent posting. By the end, you’ll have a self-running system that shares your best food photos effortlessly. Why Does It Matter? Manual photo sharing is time-consuming and inconsistent. Here’s why this workflow is a game changer: Zero Human Error**: AI ensures consistent captions and posting accuracy. Time-Saving Automation**: Automatically handle photo sharing, boosting efficiency. Scheduled Posting**: Maintain a regular presence on Pinterest without manual effort. Focus on Creativity**: Free your team from repetitive posting tasks. Think of it as your tireless social media assistant that keeps your Pinterest feed vibrant. How It Works Here’s the step-by-step magic behind the automation: Step 1: Trigger the Workflow Detect new photos to post using the Daily Post Scheduler node (e.g., once daily). Initiate the workflow at a scheduled time to check for new food photos. Step 2: Fetch Food Photos from Sheet Retrieve rows from the Google Sheet that contain food photo metadata like image URLs, ratings, and status. Step 3: Filter 4+ Star Dishes Filter only those food entries with high ratings (4 stars or above) and unposted status. Step 4: AI Caption Generator Use AI (e.g., GPT/OpenAI) to create engaging and relevant captions for selected food photos. Step 5: Upload to Pinterest Automatically post the food photo with the generated caption to Pinterest via the Pinterest API. Step 6: Mark as Posted in Sheet Update the Google Sheet to reflect that the photo has been successfully shared. How to Use the Workflow? Importing a workflow in n8n is a straightforward process that allows you to use pre-built workflows to save time. Below is a step-by-step guide to importing the Automated Food Photo Sharing workflow in n8n. Steps to Import a Workflow in n8n Obtain the Workflow JSON Source the Workflow: Workflows are shared as JSON files or code snippets, e.g., from the n8n community, a colleague, or exported from another n8n instance. Format: Ensure you have the workflow in JSON format, either as a file (e.g., workflow.json) or copied text. Access the n8n Workflow Editor Log in to n8n (via n8n Cloud or self-hosted instance). Navigate to the Workflows tab in the n8n dashboard. Click Add Workflow to create a blank workflow. Import the Workflow Option 1: Import via JSON Code (Clipboard): Click the three dots (⋯) in the top-right corner to open the menu. Select Import from Clipboard. Paste the JSON code into the text box. Click Import to load the workflow. Option 2: Import via JSON File: Click the three dots (⋯) in the top-right corner. Select Import from File. Choose the .json file from your computer. Click Open to import. Setup Notes Google Sheet Columns**: Ensure your Google Sheet includes the following columns: Image URL, Rating (numeric, e.g., 1-5), Feedback (text), Pin Title, Pin Description, Destination URL, Board ID, and Status (e.g., "Pending" or "Posted"). Google Sheets Credentials**: Configure OAuth2 settings in the Fetch Food Photos node with your Google Sheet ID and credentials. AI Model**: Set up the AI Caption Generator node with OpenAI credentials (e.g., API key). Pinterest API**: Authorize the Upload to Pinterest node with Pinterest API credentials (e.g., Bearer Token) and obtain the Board ID. Scheduling**: Adjust the Daily Post Scheduler node to your preferred posting time (e.g., daily at 9 AM).
by Mohan Gopal
Personalized Tour Package Recommendations via n8n + Pinecone + Lovable UI I've created an intelligent Travel Itinerary Planner that connects a Lovable front-end UI with a smart backend powered by n8n, Pinecone, and OpenAI to deliver personalized tour packages based on natural language queries. What It Does Users type in their travel destination and duration (e.g., "Paris 5 days trip" or "Bali Trip for 7 Days, would love water sports, adventures and trekking included, also some historical monuments") through a Lovable UI. This triggers a webhook in n8n, which processes the request, searches vectorized tour data in Pinecone, and generates a personalized itinerary using OpenAI’s GPT. The results are then structured and sent back to the frontend UI for display in an interactive, reorderable format. Workflow Architecture Lovable UI ➝ Webhook ➝ Tour Recommendation Agent ➝ Vector Search ➝ OpenAI Response ➝ Structured Output ➝ Response to Lovable Tools & Components Used Webhook Acts as the entry point between the Lovable frontend and n8n. Captures the user query (destination, duration) and forwards it into the workflow. OpenAI Chat Model To interpret the user query. To generate a user-friendly, structured tour package from the matched results. Simple Memory Keeps chat state and context for follow-up queries (extendable for future features like multi-step planning or saved itineraries). Question Answering with Vector Store Searches vector embeddings of pre-loaded tour data. Finds the most relevant tour packages by comparing query embeddings. Pinecone Vector Store Stores tour packages and activity data in vectorized format. Enables fast and scalable semantic search across destinations, themes (e.g., "adventure", "cultural"), and duration. OpenAI Embeddings Embeds all tour and activity documents stored in Pinecone. Converts input user queries into embedding vectors for semantic search. Structured Output Parser Parses the final OpenAI-generated response into a consistent, frontend-consumable JSON format. Frontend (Lovable UI) User types in destination or their travel package needs in the Tour Search. Lovable queries the n8n workflow. Displays beautifully structured, editable itineraries. How to Set It Up Webhook Setup in n8n Create a POST webhook node. Set Webhook URL and connect it with Lovable frontend. Pinecone & Embeddings Convert your static tour package documents (PDFs, JSON, CSV, etc.) into embeddings using OpenAI. Store the embeddings in a Pinecone namespace (e.g., kuala-lumpur-3-days). Configure “Answer with Vector Store” Tool Connect the tool to your Pinecone instance and pass query embedding for matching. Connect to OpenAI Chat Use the GPT model to process query + context from Pinecone to generate an engaging itinerary description. Optionally chain a second model to format it into UI-consumable output. Output Parser & Return Use Structured Output Parser to parse the response and pass it to Respond to Webhook node for UI display. Ideal Use Cases Smart itinerary planning for OTAs or DMCs Personalized travel recommendations in chatbots or apps Travel advisors and agents automating package generation Benefits Highly relevant, contextual travel suggestions Natural query understanding via OpenAI Seamless frontend-backend integration via Webhook If you’re building personalized experiences for travelers using AI, give this approach a try! Let me know if you’d like the JSON for this workflow or help setting up the Pinecone data pipeline.
by Jimleuk
This n8n template demonstrates how you can leverage existing support site search to power your Support Chatbots and agents. Building a support chatbot need not be complicated! If building and indexing vector stores or duplicating data isn't necessarily your thing, an alternative implementation of the RAG approach is to leverage existing knowledge-bases such as support portals. In this way, document management and maintenance of your support agent is significantly reduced. Disclaimer: This template example uses AcuityScheduling's help center website but is not associated, supported nor endorsed by the company. How it works A simple AI agent is connected with chat trigger to receive user queries. The AI agent is instructed to fetch information from the knowledge-base via the attached custom workflow tool (aka "knowledgebase tool"). There is no step to replicate the entire support articles database into a vector store. You may choose not too because of time, cost and maintainence involved. Instead, the tool leverages the existing support portal's search API to retrieve knowledge-base articles. Finally, the search results are formatted before sending an aggregated response back to the agent. How to use? Customise the subworkflow to work with your own support portal API and format accordingly. Try the following queries How do I connect my icloud to acuityScheduling? How do I download past invoices for my Acuity account? Requirements OpenAI for LLM. If your organisation's APIs require authorisation, you may need to add custom credentials as necessary. Customising this workflow Add additional tools to reach other parts of your internal knowledgebase. Not using OpenAI? Feel free to swap but ensure the LLM has tools/function calling support.
by Jimleuk
This n8n workflow demonstrates how to create a really simple yet effective customer support channel and pipeline by combining Slack, Linear and AI tools. Built on n8n's ability to integrate anything, this workflow is intended for small support teams who want to maximise re-use of the tools they already have with an interface which is doesn't require any onboarding. Read the blog post here: https://blog.n8n.io/automated-customer-support-tickets-with-n8n-slack-linear-and-ai/ How it works The workflow is connected to a slack channel setup with the customer to capture support issues. Only messages which are tagged with a "✅" reaction are captured by the workflow. Messages are tagged by the support team in the channel. Each captured support issue is sent to the AI model to classify, prioritise and rewrite into a support ticket. The generated support ticket is uploaded to Linear for the support team to investigate and track. Support team is able to report back to the user via the channel when issue is fixed. Requirements Slack channel to be monitored Linear account and project Customising this workflow Don't have Linear? This workflow can work just as well with traditional ticketing systems like JIRA.
by Don Jayamaha Jr
📰 This AI-powered agent performs real-time sentiment analysis on Tesla (TSLA) news to support trading decisions. It aggregates headlines from 5 trusted sources and uses DeepSeek Chat to classify sentiment and generate structured summaries. This tool is a critical sub-agent in the broader Tesla Quant Trading AI Agent system. ⚠️ Not standalone — this agent is designed to be executed by the Tesla Quant Trading AI Agent. ⚙️ Requires: DeepSeek Chat API Key 🔌 Workflow Role This tool processes Tesla-related news and produces output like: { "sentiment": "bullish", "summary": "Tesla stock rallied today after strong delivery numbers and Cybertruck updates. Analysts remain optimistic.", "topHeadlines": [ "Tesla beats Q2 delivery forecast – Yahoo Finance", "Cybertruck ramps up in Texas – Electrek", "Berlin Gigafactory expands battery production – CleanTechnica" ] } Its output feeds directly into the master trading agent’s final trade report. 📰 News Sources Used This agent collects real-time headlines from: Google News (filtered by “Tesla” or “TSLA”) Yahoo Finance (TSLA-specific feed) Electrek (Tesla archive) CleanTechnica (Tesla sustainability news) TeslaNorth (app/product release updates) These five tools are always queried together to ensure market-wide signal coverage. 🤖 What the Agent Does Pulls headlines from all 5 Tesla-specific RSS feeds Uses DeepSeek Chat to: Analyze narrative tone (bullish / bearish / neutral) Identify macro/financial drivers Generate a 2–3 sentence summary Return top 3–5 headlines Outputs structured JSON for downstream use 🛠️ Setup Instructions 1. Install & Name Import this file and name it: Tesla_News_and_Sentiment_Analyst_Tool 2. Add DeepSeek API Credentials Go to: Credentials → Add New → DeepSeek API Save as: DeepSeek account 3. Internet Access Required Ensure RSS feeds can fetch live headlines Works best with a cloud-hosted n8n instance or tunnel-enabled local install 4. Must Be Triggered by Parent Triggered via Execute Workflow by the Tesla Quant Trading AI Agent Requires these inputs: message: optional query context sessionId: passed to maintain short-term memory across executions 🧠 Agent Architecture | Node Name | Function | | ---------------------------------- | ------------------------------------------------ | | DeepSeek Chat Model | Performs AI-based sentiment analysis | | Tesla News and Sentiment Analyst | Combines results, formats output in strict JSON | | Simple Memory | Stores session-level context (short-term memory) | | 5x RSS nodes | Aggregate Tesla news from trusted media outlets | 📌 Sticky Notes Included 🟢 Trigger from Parent Workflow – Executed only by main TSLA agent 🟠 News Feeds Overview – Lists and explains each of the 5 feeds 🧠 DeepSeek Chat Notes – Describes LLM behavior and parsing role 🔵 Short-Term Memory – Buffers sentiment context during user session 📘 Sentiment Analyst Agent – Summarizes key responsibilities 📎 Licensing & Attribution © 2025 Treasurium Capital Limited Company This architecture, workflow structure, and prompt design are licensed for educational and operational use only. Commercial resale or rebranding prohibited without authorization. 🔗 Creator: Don Jayamaha 🔗 Templates: https://n8n.io/creators/don-the-gem-dealer/ 🚀 Power your TSLA trading with AI-driven sentiment—built with DeepSeek Chat and 5 trusted news sources. This tool is required by the Tesla Quant Trading AI Agent.
by Jimleuk
This n8n template demonstrates how easy it is to build an Outlook Calendar Assistant powered by an AI agent equipped with Tools. For teams using Outlook Calendar and Slack who need easier calendar management, this workflow can be a great first step to introducing powerful AI tools into your daily activities. How it works A Slack Trigger node is configured to catch "bot mentions" events in a designated channel. The message is parsed using the Edit fields node to extract only the required attributes of the event. An AI Agent equipped with Outlook Calendar Tools enables question and answer capability for the organisation's shared calendars and events. The AI agent's response is sent back to Slack as a reply to the user's query. How to use The workflow is triggered via @mention-ing the bot followed by the query. eg. "@bot how many meetings does Paul have to attend to this week?" To start listening to real mentions, you must activate the workflow and set it to production mode. You must use the production webhook URL for the event subscription. Some sample queries to try "What's included in the product team's sprint demo this week?" "Who's booked room 7 for this Thursday?" "When is Jim & Nik's sales meeting with Microsoft?" Requirements Slack for Chat and Trigger. To get connected to Slack, see the official n8n docs for Slack Credentials. Outlook for Agent Tools To get connected to Outlook, see the official n8n docs for Outlook Credentials. Customising this workflow Not using Slack? This template can be modified to work with Teams but requires a little more configuration. Agents can have any number of tools but an overloaded agent is prone to confusion! If this happens, try splitting into multiple agents serving separate needs.
by Jimleuk
This n8n workflow is a fun way to query and search over your credentials on your n8n instance. Good to know Your credentials should remain safe as this workflow does not decrypt or use any decrypted data. Example Usage "Which workflows are using Slack and Google Calendar?" "Which workflows have AI in their name but are not using openAI?" How it works Using the n8n API, it fetches all workflow data on the instance. Workflow data contains references to credentials used so this will be extracted. With some necessary reformatting, the workflows and their credentials metadata are stored to a SQLite database. Next, an AI agent is used with a custom SQL tool that reads the SQLite database created in the previous step. The AI agent is instructed to perform SQL queries against our workflow credential table when asked about credentials by the user. Requirements You'll need an n8n API key. Please note that only workflows will be scoped to your API key. Customising the workflow Add extra table fields to the SQLite database to answer even more complex queries such as: workflow status to differentiate between active and inactive workflows.
by Tharwat Mohamed
Document-Aware WhatsApp AI Bot for Customer Support Google Docs-Powered WhatsApp Support Agent 24/7 WhatsApp AI Assistant with Live Knowledge from Google Docs 📝Description Template Smart WhatsApp AI Assistant Using Google Docs Help customers instantly on WhatsApp using a smart AI assistant that reads your company’s internal knowledge from a Google Doc in real time. Built for clubs, restaurants, agencies, or any business where clients ask questions based on a policy, FAQ, or services document. ⚙️ How it works Users send free-form questions to your WhatsApp Business number (e.g. “What are the gym rules?” or “Are you open today?”) The bot automatically reads your company’s internal Google Doc (policy, schedule, etc.) It merges the document content with today’s date and the user’s question to craft a custom AI prompt The AI (Gemini or ChatGPT) then replies back on WhatsApp using natural, helpful language All conversations are logged to Google Sheets for reporting or audit > 💡Bonus: The AI even understands dates inside the document and compares them to today’s date — e.g. if your document says “Closed May 25 for 30 days,” it will say “We're currently closed until June 24. 🧰 Set up steps Connect your WhatsApp Cloud API account (Meta) Add your Google account and grant access to the Doc containing your company info Choose your AI model (ChatGPT/OpenAI or Gemini) Paste your document ID into the Google Docs node Connect your WhatsApp webhook to Meta (only takes 5 minutes) Done — start receiving and answering customer questions! > 📄 Works best with free-tier OpenAI/Gemini, Google Docs, and Meta's Cloud API (no phone required). Everything is modular, extensible, and low-code. 🔄 Customization Tips Change the Google Doc anytime to update answers — no retraining needed Add your logo and business name in the AI agent’s “System Prompt” Add fallback routes like “Escalate to human” if the bot can't help Clone for multiple brands by duplicating the workflow and swapping in new docs 🤝 Need Help Setting It Up? If you'd like help connecting your WhatsApp Business API, setting up Google Docs access, or customizing this AI assistant for your business or clients… 📩 I offer setup, branding, and customization services: WhatsApp Cloud API setup & verification Google OAuth & Doc structure guidance AI model configuration (OpenAI / Gemini) Branding & prompt tone customization Logging, reporting, and escalation logic Just send a message via: Email: tharwat.elsayed2000@gmail.com WhatsApp: +20 106 180 3236
by David Olusola
🔍 What This Workflow Does This RAG Pipeline in n8n automates document ingestion from Google Drive, vectorizes it using OpenAI embeddings, stores it in Pinecone, and enables chat-based retrieval using LangChain agents. Main Functions: 📂 Auto-detects new files uploaded to a specific Google Drive folder. 🧠 Converts the file into embeddings using OpenAI. 📦 Stores them in a Pinecone vector database. 💬 Allows a user to query the knowledge base through a chat interface. 🤖 Uses a GPT-4o-mini model with LangChain to generate intelligent responses using retrieved context. ⚙️ Setup Instructions Connect Accounts Ensure these services are connected in n8n: ✅ Google Drive (OAuth2) ✅ OpenAI ✅ Pinecone You can do this in n8n > Credentials > New and use the matching names from the file: Google Drive: "Google Drive account 2" OpenAI: "OpenAi success" Pinecone: "PineconeApi account 2" Folder Setup Upload your documents to this folder in Google Drive: 📁 Power Folder The workflow is triggered every minute when a new file is uploaded. Workflow Overview A. File Ingestion Path Google Drive Trigger — detects new file. Google Drive (Download) — downloads the new file. Recursive Text Splitter — splits text into chunks. Default Data Loader — loads content as LangChain documents. OpenAI Embeddings — converts text chunks into embeddings. Pinecone Vector Store — stores them in "ragfile" index. B. Chat Retrieval Path When chat message received — AI Agent — LangChain agent managing tools. OpenAI Chat Model (GPT-4o-mini) — generates replies. Pinecone Vector Store (retrieval) — retrieves matching content. Embeddings OpenAI1 — helps match queries to document chunks.
by Automate With Marc
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 🚀 Start-up Ideas Generator – From Idea to Executive Summary in Minutes Description: This AI-powered n8n workflow helps you brainstorm and validate start-up ideas, perform market research using Perplexity Sonar, and automatically generate a professional business plan — all within minutes. It’s designed for entrepreneurs, accelerators, venture studios, and ideation teams looking to go from a single prompt to a detailed proposal ready to pitch or develop. Watch step-by-step build video of n8n workflows like this: https://www.youtube.com/@Automatewithmarc Setup Instructions Required Credentials: OpenAI API Key – For GPT-4.1 Perplexity Sonar Access Token – For deep research Anthropic Claude API Key – For business plan writing Google Service Account Credentials – To write to Docs How It Works: 💬 Chat Trigger Start the process by typing a simple request like “Give me a few start-up ideas in AI tech.” 🧠 AI Research Agent (OpenAI + Perplexity Sonar) The system defines the research scope, taps into Perplexity AI for deep market scans, and outputs high-potential business opportunities including: Market size Customer pain points Competition overview Differentiation strategy 📄 Business Case Generator (Claude) Each opportunity is expanded into a complete business plan using Claude Sonnet, covering: Executive summary Market analysis Product description Competitor gap Business model & GTM Financials & roadmap 📃 Google Docs Export The full business plan is automatically inserted into a connected Google Doc for easy sharing, editing, or pitching. Google Docs Configuration: Create a Google Doc titled "Startup Business Plan" (or adjust the title in the node settings) Share the document with your Google service account email Update the Document ID field in the Google Docs node accordingly Ensure that the structure accepts plain text input — formatting is handled by the node Tools & Models Used: LangChain Chat Trigger OpenAI GPT-4.1 (Research Prompt Structuring) Perplexity Sonar Deep Research (Market Research) Anthropic Claude Sonnet (Business Plan Writing) Google Docs Node (Formatted Output) Use Cases: Rapid ideation for venture building or incubators Validating start-up ideas before prototyping Automating market research + proposal writing Generating investor-ready pitch materials