Workflow Templates

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.

This guide will show you how to use a workflow as a reusable tool in n8n, such as integrating an AI Agent or other specialized processes into your workflows. By the end of this example, you'll have a simple, reusable workflow that can be easily plugged into larger projects, making your automations more efficient and scalable. With this approach, you can create reusable workflows like "Scrape a Page," "Search Brave," or "Generate an Image," which you can then call whenever needed. While n8n makes it easy to build these workflows from scratch, setting them up as reusable components saves time as your automations grow in complexity. Setup Add the "Execute Workflow Trigger" node Add the node(s) to perform the desired tasks in the workflow Add a final "Set" or "Edit Fields" node at the end to ensure all external workflows return a consistent output format Details In this example, the "Execute Workflow Trigger" expects input in the following JSON format: [ { "query": { "url": "https://en.wikipedia.org/wiki/some_info" } } ] Once your external workflow is ready, you can instruct the AI Agent to use this tool by connecting it to the external workflow. Set up the schema type to "Generate from JSON Example" using this structure: { "url": "URL_TO_GET" } Finally, ensure your external workflow includes a "Set" or "Edit Fields" node at the end to define the response format. This helps keep the outputs of your reusable workflows consistent and predictable.

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.

AI evaluation in n8n This is a template for n8n's evaluation feature. Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow. By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't. How it works This template shows how to calculate a workflow evaluation metric: whether a category matches the expected one. The workflow takes support tickets and generates a category and priority, which is then compared with the correct answers in the dataset. We use an evaluation trigger to read in our dataset It is wired up in parallel with the regular trigger so that the workflow can be started from either one. More info Once the category is generated by the agent, we check whether it matches the expected one in the dataset Finally we pass this information back to n8n as a metric

📰 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.

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.

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.

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

This n8n workflow automates SEO keyword research by querying the Ahrefs API for keyword data and related keyword insights. The enriched data is then processed by an AI agent to format a response and provide valuable SEO recommendations. Perfect for SEO specialists, content marketers, digital agencies, and anyone looking to gain valuable insights into keyword opportunities to boost their rankings. ⚙️ How This Workflow Works This workflow guides you through the entire SEO keyword research process, from entering the initial keyword to receiving detailed insights and related keyword suggestions. 1. 🗣️ User Input (Keyword Query) The user enters a keyword they want to research. This input is captured by the Chat Input Node, ready for analysis. 2. 🤖 AI Agent (Input Verification) The AI Agent reviews the keyword input for any grammatical errors or extra commentary. If necessary, it cleans the input to ensure a seamless query to the API. 3. 🔑 Ahrefs API (Keyword Data Retrieval) The cleaned keyword is sent to the Ahrefs Keyword Tool API. This retrieves a detailed report including metrics like search volume, keyword difficulty, and CPC. 4. 💡 Related Keywords Extraction (Using JavaScript Function) The workflow uses a JavaScript function to extract main keyword data and 10 related keywords data from the Ahrefs response. You can tweak the script to adjust the number of related keywords or the level of detail you want. 5. 🧠 AI Agent (Text Formatting) The aggregated data, including both the main keyword and related keywords, is sent to an AI agent. The AI agent formats the data into a concise, readable format that can be shared with the user. 6. 📨 Final Response The formatted text is delivered to the user with keyword insights, recommendations, and related keyword suggestions. ✅ Smart Retry & Error Handling Each subworkflow includes a fail-safe mechanism to ensure: ✅ Proper error handling for any issues with the API request. 🕒 Failed API requests are retried after a customizable period (e.g., 2 hours or 1 day). 💬 User input validation prevents any incorrect or malformed queries from being processed. 📋 Ahrefs API Setup To use this workflow, you’ll need to set up your Ahrefs API credentials: 🔑 Ahrefs API Sign up for an Ahrefs account and get your key here: Ahrefs Keyword Tool API Once signed up, you'll receive an API key, which you’ll use in the x-rapidapi-key header in n8n. Ensure you check the Ahrefs Keyword Tool API documentation for more details on available parameters. 📥 How to Import This Workflow Copy the json code. Open your n8n instance. Open a new workflow. Paste anywhere inside the workflow. Voila. 🛠️ Customization Options Adjust the number of related keywords extracted (default is 10). Customize the AI agent response formatting or add specific recommendations for users. Modify the JavaScript function to extract different metrics from the Ahrefs API. 🧪 Use Case Example Trying to optimize your blog post around a specific keyword? Query a broad keyword, like “SEO tips”. Get related keyword data and search volume insights. Use the AI agent to provide keyword recommendations and additional topics to target. 💥 Boost your content strategy with fresh keywords and relevant search data!

🤖 GitHub Auto-Assign Bot Streamline your open source project with intelligent issue assignment automation. ✨ What It Does Automatically assigns GitHub issues to contributors who comment "assign me" - eliminating manual triage work and creating a fair, first-come-first-served system. 🔑 Key Features Smart Detection**: Monitors both new issues and comments for assignment requests Conflict Prevention**: Checks existing assignments before making new ones Auto-Labeling**: Adds "assigned" labels for better tracking Self-Service Assignment**: Contributors claim issues with simple "assign me" command Polite Responses**: Automatically notifies when issues are already assigned 🎯 Perfect For Open source maintainers Development teams managing GitHub repos Projects with active contributor communities Anyone reducing manual issue management ⚙️ Setup Requirements GitHub repository with issues enabled n8n instance with GitHub OAuth credentials 5 minutes configuration time 🚀 How Contributors Use It Find an unassigned issue Comment assign me Get automatically assigned Start coding immediately → no maintainer approval needed! ✅ Benefits Reduces maintainer workload** - No manual assignments Faster contributor onboarding** - Instant self-service Prevents conflicts** - Built-in assignment checking Scales automatically** - Works across unlimited issues Improves contributor experience** - Simple, clear process ⚡ Workflow Triggers New GitHub issues containing "assign me" New comments with "assign me" on existing issues Automatic label management Conflict resolution responses > Transform your GitHub workflow - Perfect for growing open source projects and development teams!

🔍 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.

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