Workflow Templates

Functionality This workflow automates the handling of incoming emails by summarizing their content, generating appropriate responses, and validating the responses through a "Human-in-the-Loop" system. It integrates with IMAP email services (e.g., Gmail, Outlook) and uses AI models to streamline the email response process. The workflow ensures that all AI-generated responses are reviewed by a human before being sent, maintaining a high level of professionalism and accuracy. This approach is particularly useful for businesses that receive a high volume of emails and need to respond quickly while ensuring quality control. How It Works Email Trigger: The workflow starts with the Email Trigger (IMAP) node, which monitors an email inbox for new messages. When a new email arrives, it triggers the workflow. Email Preprocessing: The Markdown node converts the email's HTML content into plain text for easier processing by the AI models. Email Summarization: The Email Summarization Chain node uses an AI model (OpenAI) to generate a concise summary of the email. The summary is limited to 100 words and is written in a professional tone. Email Response Generation: The Write email node uses an AI model (OpenAI) to draft a professional response to the email. The response is based on the email content and is limited to 100 words. Human-in-the-Loop Approval: The Set Email text node prepares the drafted response for approval. The Approve Email node sends the drafted response to a human approver (e.g., an internal email address) for review. The email includes: The original message. The AI-generated response. The Approved? node checks if the response has been approved by the human reviewer. If approved, the workflow proceeds to send the response; otherwise, it stops. Sending the Response: The Send Email node sends the approved response back to the original sender. Key Features Automated Email Summarization**: Summarizes incoming emails to provide a quick overview of the content. AI-Powered Response Generation**: Drafts professional responses to emails using AI. Human-in-the-Loop Approval**: Ensures all AI-generated responses are reviewed and approved by a human before being sent. IMAP Integration**: Works with IMAP email services like Gmail and Outlook. Efficient Email Management**: Reduces the time and effort required to handle incoming emails while maintaining high-quality responses. This workflow is ideal for businesses looking to automate their email response process while maintaining control over the quality of outgoing communications. It leverages AI to handle repetitive tasks and ensures that all responses are reviewed by a human, providing a balance between automation and human oversight. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Fact-Checking Workflow Documentation Overview This workflow is designed for automated fact-checking of texts. It uses AI models to compare a given text with a list of facts and identify potential discrepancies or hallucinations. Components 1. Input The workflow can be initiated in two ways: a) Manually via the "When clicking 'Test workflow'" trigger b) By calling from another workflow via the "When Executed by Another Workflow" trigger Required inputs: facts: A list of verified facts text: The text to be checked 2. Text Preparation The "Code" node splits the input text into individual sentences Takes into account date specifications and list elements 3. Fact Checking Each sentence is individually compared with the given facts Uses the "bespoke-minicheck" Ollama model for verification The model responds with "Yes" or "No" for each sentence 4. Filtering and Aggregation Sentences marked as "No" (not fact-based) are filtered The filtered results are aggregated 5. Summary A larger language model (Qwen2.5) creates a summary of the results The summary contains: Number of incorrect factual statements List of incorrect statements Final assessment of the article's accuracy Usage Ensure the "bespoke-minicheck" model is installed in Ollama (ollama pull bespoke-minicheck) Prepare a list of verified facts Enter the text to be checked Start the workflow The results are output as a structured summary Notes The workflow ignores small talk and focuses on verifiable factual statements Accuracy depends on the quality of the provided facts and the performance of the AI models Customization Options The summarization function can be adjusted or removed to return only the raw data of the issues found The AI models used can be exchanged if needed This workflow provides an efficient method for automated fact-checking and can be easily integrated into larger systems or editorial workflows.

The n8n Nostr Community Node is a tool that integrates Nostr functionality into n8n workflows, allowing users to interact with the Nostr protocol seamlessly. It provides both read and write capabilities and can be used for various automation tasks. Disclaimer This node is ideal for self-hosted n8n setups, as ++community nodes are not supported on n8n cloud++. It opens up exciting possibilities for integrating workflows with the decentralized Nostr protocol. n8n Community Node for Nostr n8n-nodes-nostrobots Features Write Operations**: Send notes and events (kind1) to the Nostr network. Read Operations**: Fetch events based on criteria such as event ID, public key, hashtags, mentions, or search terms. Utility Functions**: Convert events into different formats like naddr or nevent and handle key transformations between bech32 and hex formats. Trigger Events**: Monitor the Nostr network for specific mentions or events and trigger workflows automatically. Use Cases Automating note posting without exposing private keys. Setting up notifications for mentions or specific events. Creating bots or AI assistants that respond to mentions on Nostr. Installation Install n8n on your system. Add the Nostr Community Node to your instance. Configure your credentials using a Nostr secret key (supports bech32 or hex formats).

iMessage AI-Powered Smart Calorie Tracker > 📌 What it looks like in use: > This image shows a visual of the workflow in action. Use it for reference when replicating or customizing the template. This n8n template transforms a user-submitted food photo into a detailed, friendly, AI-generated nutritional report — sent back seamlessly as a chat message. It combines OpenAI's visual reasoning, Postgres-based memory, and real-time messaging with Blooio to create a hands-free calorie and nutrition tracker. 🧠 Use Cases Auto-analyze meals based on user-uploaded images. Daily/weekly/monthly diet summaries with no manual input. Virtual food journaling integrated into messaging apps. Nutrition companion for healthcare, fitness, and wellness apps. 📌 Good to Know ⚠️ This uses GPT-4 with image capabilities, which may incur higher usage costs depending on your OpenAI pricing tier. Review OpenAI’s pricing. The model uses visual reasoning and estimation to determine nutritional info — results are estimates and should not replace medical advice. Blooio is used for sending/receiving messages. You will need a valid API key and project set up with webhook delivery. A Postgres database is required for long-term memory (optional but recommended). You can use any memory node with it. ⚙️ How It Works Webhook Trigger The workflow begins when a message is received via Blooio. This webhook listens for user-submitted content, including any image attachments. Image Validation and Extraction A conditional check verifies the presence of attachments. If images are found, their URLs are extracted using a Code node and prepared for processing. Image Analysis via AI Agent Images are passed to an OpenAI-based agent using a custom system prompt that: Identifies the meal, Estimates portion sizes, Calculates calories, macros, fiber, sugar, and sodium, Scores the meal with a health and confidence rating, Responds in a chatty, human-like summary format. Memory Integration A Postgres memory node stores user interactions for recall and contextual continuity, allowing day/week/month reports to be generated based on cumulative messages. Response Aggregation & Summary Messages are aggregated and summarized by a second AI agent into a single concise message to be sent back to the user via Blooio. Message Dispatch The final message is posted back to the originating conversation using the Blooio Send Message API. 🚀 How to Use The included webhook can be triggered manually or programmatically by linking Blooio to a frontend chat UI. You can test the flow using a manual POST request containing mock Blooio payloads. Want to use a different messages app? Replace the Blooio nodes with your preferred messaging API (e.g., Twilio, Slack, Telegram). ✅ Requirements OpenAI API access with GPT-4 Vision or equivalent multimodal support. Blooio account with access to incoming and outgoing message APIs. Optional: Postgres DB (e.g., via Neon) for tracking message context over time. 🛠️ Customising This Workflow Prompt Tuning** Tailor the system prompt in the AI Agent node to fit specific diets (e.g., keto, diabetic), age groups, or regionally-specific foods. Analytics Dashboards** Hook up your Postgres memory to a data visualization tool for nutritional trends over time. Multilingual Support** Adjust the response prompt to translate messages into other languages or regional dialects. Image Preprocessing** Insert a preprocessing node before sending images to the model to resize, crop, or enhance clarity for better results.

Automated AI Research Assistant: From Query to Polished Report with Jina & Gemini Turn a single research question into a comprehensive, multi-source report with proper citations. This workflow automates the entire research process by leveraging the web-crawling power of Jina AI and the advanced reasoning capabilities of Google's Gemini models. Simply input your query, and this AI-powered assembly line will search the web, scrape relevant sources, summarize the content, draft a structured research paper, and finally, evaluate and polish the report for accuracy and formatting. ✨ Key Features 🔎 Dynamic Web Search**: Kicks off by searching the web with Jina AI based on your initial query. 📚 Multi-Source Content Scraping**: Automatically reads and extracts content from the top 10 search results. 🧠 AI-Powered Summarization**: Uses a Gemini agent to intelligently summarize each webpage, retaining the core information. ✍️ Automated Report Generation**: A specialized "Generator Agent" synthesizes the summarized data into a structured research paper, complete with an executive summary, introduction, discussion, and conclusion. ✅ Citation & Quality Verification**: A final "Evaluator Agent" meticulously checks the generated report for citation accuracy, logical flow, and markdown formatting, delivering a polished final document. 📈Rate-Limit Ready**: Includes a configurable Wait node to ensure stable execution when dealing with multiple API calls. 📝 What This Workflow Does This workflow is designed to be your personal research assistant. It addresses the time-consuming process of gathering, reading, and synthesizing information from multiple online sources. Instead of spending hours manually searching, reading, and citing, you can delegate the entire task to this workflow and receive a well-structured and cited report as the final output. It's perfect for students, researchers, content creators, and analysts who need to quickly compile information on any given topic. ⚙️ How It Works (Step-by-Step) Initiate with a Query: The workflow starts when you send your research question or topic to the Chat Trigger node. Search the Web: The user's query is passed to the Jina AI node, which performs a web search and returns the top 10 most relevant URLs. Scrape, Summarize, Repeat: The workflow then loops through each URL: Read Content: The Jina AI node scrapes the full text content from the URL. Summarize: A Summarizer Agent powered by Google Gemini reads the scraped content and the original user query, then generates a concise summary. Wait: A one-second pause helps to avoid hitting API rate limits before processing the next URL. Aggregate the Knowledge: Once the loop is complete, a Code node gathers all 10 individual summaries into a single, neatly structured list. Draft the Research Report: This aggregated data is fed to the Generator Agent. Following a detailed prompt, this Gemini-powered agent writes a full research report, structuring it with headings and adding inline citations for every piece of information it uses. Evaluate and Finalize: The generated draft is passed to the final Evaluator Chain. This agent acts as a quality control supervisor. It verifies that all claims are correctly cited, refines the content for clarity and academic tone, and polishes the markdown formatting to produce the final, ready-to-use report. 🚀 How to Use This Workflow Credentials: Click on Use template, then configure your credentials for the following nodes: Jina AI: You will need a Jina AI API key for the Search web and Read URL content nodes. Get your key from here: JinaAI API Key Google Gemini: You will need a Google Gemini API key for the Summarizer Model, Generator Model, and Evaluator Model nodes. Get your key from here: Gemini API Key Activate Workflow: Make sure the workflow is active in your n8n instance. Start Research: Send a chat message with your research topic to the webhook URL provided in the When chat message received node. Get Your Report: Check the output of the final node, Evaluator Chain, to find your completed and polished research report. Nodes Used Chat Trigger Jina AI Code (Python) Split in Batches (Looping) Wait AI Agent Basic LLM Chain Google Gemini Chat Model

Overview This n8n workflow processes user feedback automatically, tags it with sentiment, and links it to relevant insights in Notion. It uses GPT-4 to analyze each feedback entry, determine whether it corresponds to an existing insight or a new one, and update the Notion databases accordingly. It helps teams centralize and structure qualitative user feedback at scale. Who It’s For Product teams looking to organize and prioritize user feedback. Founders or solo builders seeking actionable insights from qualitative data. Anyone managing a Notion workspace where feedback is collected and needs to be tagged or linked to features and improvements. Prerequisites A Notion account with: A Feedback database (must include fields for feedback content and status). An Insights database with multi-select fields for Solution, User Persona, and a relation to Feedback. The Notion template (linked below) helps you get started quickly — just remove the mock data. A configured Notion API integration in n8n. 👉 Don’t forget to connect the n8n integration to the correct Notion page. An OpenAI API key Notion Template This workflow is designed to work seamlessly with a pre-configured Notion template that includes the required feedback and insights structure. 👉 User Feedback Analysis – Notion Template How It Works The workflow is triggered when a feedback item is updated in Notion (e.g. new feedback is submitted). Sentiment analysis (Positive, Neutral, or Negative) is run using OpenAI and stored in a select field in Notion. The AI agent analyzes the feedback to: Identify whether it matches an existing insight. Or create a new insight in Notion with a concise name, solution, and user persona. The feedback is then linked to the appropriate insight and marked as "Processed." How to Use It Connect your Notion databases in all Notion nodes (including those used by the AI agent) for both Feedback and Insights — follow the node names provided. Ensure your OpenAI and Notion credentials are correctly set. Set up your product context: Define a “Product Overview” and list your “Core Features”. This helps the AI agent categorize insights more accurately. (The Basecamp product is used as an example in the template.) (Optional) Modify the prompt to better fit your specific product context. Once feedback is added or updated in Notion, the workflow triggers automatically. Notes Only feedback with the status Received is processed. New insights are only created if no relevant match is found. Feedback is linked to insights via Notion’s relation property. A fallback parser is included to fix potential formatting issues in the AI output. You can swap the default n8n memory for a more robust backend like Supabase. 🙏 Please share your feedback with us. It helps us tremendously!

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Telegram Voice AI Assistant This n8n template creates a multimodal Telegram bot that dynamically responds to users: Replies with voice** when receiving voice messages (using ElevenLabs TTS) Replies with text** for text-based queries Supports custom AI tools (e.g., crypto APIs, databases, or custom functions) Built with LangChain Agents, it can integrate any external API or data source into conversations. Key Features 🎙️ Smart Response Logic Voice Query? → Voice Reply** Transcribes audio via ElevenLabs STT Processes with AI (Groq/Gemini) Converts text response to natural speech (ElevenLabs TTS) Text Query? → Text Reply** Bypasses TTS/STT for faster responses 🛠️ Extensible AI Tools Add your own tools: Database lookups Weather/stock APIs Custom Python functions RAG (document retrieval) Supports multi-step tool chaining (e.g., "Get BTC price → analyze trends → summarize") 🌐 Language & Context Auto-detects user language (via Telegram’s language_code) Maintains session memory (remembers conversation history) Use Cases Voice-first customer support** Crypto/analytics assistants** (e.g., "What’s Ethereum’s current gas fee?") Multilingual FAQ bots** Educational tutors** (voice-interactive learning) Requirements Telegram Bot Token** ElevenLabs API Key** (For TTS/STT) Groq API Key* or *Google Gemini API Key** Customization Tips Change AI personality*: Modify the *systemMessage in the Voice Assistant node Add more models**: Swap Groq/Gemini for OpenAI, Anthropic, etc. Extend functionality**: Add RAG (Retrieval-Augmented Generation) for document queries Take this template to create a Siri-like AI assistant for Telegram in minutes! 🚀

Transform static digital assets into dynamic, self-updating powerhouses that stay relevant for years to come! This workflow solves a common problem: once you publish forms, emails, or templates, their content becomes frozen in time. Users discovering them months later see outdated information, missed opportunities, and stale offers. Stop losing opportunities to stale content – make your digital assets work harder and stay fresher, automatically! Here's how it works: 🔗 Stable embed links mean your original assets never need updating 🔄 Dynamic URL redirects that automatically point to the latest pages 🖼️ Auto-updating images that showcase fresh offers or content 📅 Scheduled updates keep everything current without manual intervention Perfect for: Workflow sticky notes that become evergreen marketing billboards Registration forms with current promotions Email signatures with latest offers Website banners that stay seasonally relevant Any digital asset you want to "future-proof" The magic: Set it up once, embed the stable URLs/images in your content, then forget about it. Years later, users will still see fresh, as current information automatically pulled from your workflow. Requirements: Free accounts with GitHub (image storage) and shorten.rest (URL redirects). Both can be swapped for your preferred services. Follow me on LinkedIn for more tips on AI automation and n8n workflows!

This general-purpose sub-agent combines multiple research and automation tools to support high-impact decision-making for animal advocacy workflows. It’s designed to act as a reusable, modular unit within larger multi-agent systems—handling search, scraping, scoring, and domain-specific semantic lookup. It powers many of the advanced workflows released by Open Paws and serves as a versatile backend utility agent. 🛠️ What It Does Performs real-time Google Search using Serper Scrapes and extracts page content using Jina AI and Scraping Dog Conducts semantic search over the Open Paws knowledge base Generates OpenAI embeddings for similarity search and analysis Routes search and content analysis through OpenRouter LLMs Connects with downstream tools like the Text Scoring Sub-Workflow to evaluate message performance > 🧩 This agent is typically used as a sub-workflow in larger automations where agents need access to external tools or advocacy-specific knowledge. 🧠 Domain Focus: Animal Advocacy The agent is pre-configured to interface with the Open Paws database—an open-source, animal advocacy-specific knowledge graph—and is optimized for content and research tasks relevant to farmed animal issues, corporate campaigns, and activist communication. 🔗 Integrated Tools and APIs | Tool | Purpose | |---------------|------------------------------------------| | Serper API | Real-time Google Search queries | | Jina AI | Web scraping and content extraction | | Scraping Dog | Social media scraping where Jina is blocked | | OpenAI API | Embedding generation for semantic search | | OpenRouter | Proxy to multiple LLMs (e.g., GPT-4, Claude)| | Open Paws DB | Advocacy-specific semantic knowledge base | 📦 Use Cases Create and evaluate online content (e.g. social media, emails, petitions) for predicted performance and advocacy alignment Act as a research and reasoning agent within multi-agent workflows Automate web and social media research for real-time campaign support Surface relevant facts or arguments from an advocacy-specific knowledge base Assist communications teams with message testing and content ideation Monitor search results and scrape pages to inform rapid response messaging

Sync Notion Contacts to Google Contacts with Group Labels Overview Seamlessly transfer your Notion contacts to Google Contacts with organized group labels, simplifying your CRM management. This n8n workflow automates syncing contacts from a Notion database to Google Contacts, applying group labels based on Notion properties. It triggers on new or updated contacts, ensuring your Google Contacts are always organized without manual effort. ✨ Key Features 🔄 Automatic Sync: Updates Google Contacts when Notion entries are added or modified. 🏷️ Group Organization: Assigns labels to contacts based on Notion’s property_buy field. ✅ Duplicate Prevention: Marks synced contacts in Notion with a checkbox. 🛠️ Flexible Customization: Add fields like email in the “Map Notion Contact Fields” node. 📡 Community Nodes: Leverages Notion and Google Contacts nodes for integration. 📋 Prerequisites Required Credentials Notion API Token:** Set up OAuth2 in n8n. Get your token from Notion’s API settings. Google Contacts OAuth2:** Configure in n8n. See n8n’s Google Contacts guide. Notion Database:** Must include name, phone, labels (property_buy), and an “Added to Contacts” checkbox. Self-Hosted n8n:** Required for community nodes. 🔄 Workflow Process Trigger: Activates on new or updated Notion database entries. Fetch Data: Retrieves contact details (name, phone, labels) from Notion. Map Fields: Organizes data in the “Map Notion Contact Fields” node. Verify Groups: Checks for existing Google Contact groups; creates new ones if needed. Sync Contacts: Adds contacts to Google Contacts with labels. Update Notion: Marks contacts as synced. Result: Organized, labeled contacts in Google Contacts, updated automatically. 📊 Output Data Structure Name:** Contact’s first name from Notion. Phone:** Contact’s phone number. Group Labels:** Assigned from Notion’s property_buy field. Sync Status:** Notion checkbox updated to confirm sync. 💡 Pro Tips Real-Time Updates:** Set the Notion Trigger node to check every minute for faster syncing. Expand Fields:** Add email or other fields in the “Map Notion Contact Fields” node. Clean Labels:** Use consistent Notion labels for better Google Contacts organization. Test Small:** Start with a small dataset to verify setup. 🆘 Troubleshooting Authentication Issues:** Verify Notion and Google Contacts OAuth2 credentials. Sync Failures:** Ensure Notion database ID and field names match the workflow. Group Errors:** Check that property_buy labels are valid. 👨‍💻 Creator Information 👤 Created by: Dan Rahimi 🌐 Website: DanRahimi.com 📧 Email: Fa.Danial@gmail.com 📺 YouTube: @DanRahimi 👥 LinkedIn: Dan-Rahimi 🤝 Support & Contributions Enjoyed this workflow? Support my work or explore more: ☕ Buy Me a Coffee 📚 AI Automation Courses: Visit DanRahimi.com for more articles and tutorials about AI automation. Disclaimer: This workflow uses community nodes and requires a self-hosted n8n instance.

This workflow listens for incoming book request emails, extracts the user's intent using the Ollama LLM, queries book data (title, summary, details) via an API, and sends a personalized recommendation email. Ideal for automated book suggestions using LLMs and structured APIs, great for newsletters, reading clubs, and educational bots. How It Works Email Request: Triggers the workflow when a new email with a book request is received. Analyze Email with Ollama: Extracts user intent and book preferences using the Ollama LLM. Create Book Search Query: Generates a query based on the analyzed intent. Book Search API: Fetches book data (title, summary, details) from an API. Check API Response: Validates the API response for book availability. Handle No Book Found: Manages cases where no suitable book is found. Extract Book Summary: Pulls the summary from the API response. Wait for Summary Response: Pauses to ensure summary data is ready. Retrieve Book Details: Gathers additional book details. Format Book Data: Structures the book information for the recommendation. Enhance Data with Code: Refines the data using custom code. Generate Email Content: Creates a personalized email recommendation. Send Email: Delivers the recommendation to the user. How to Use Import the workflow into n8n. Configure email credentials for the Email Request node. Set up Ollama LLM API credentials and endpoint. Configure the Book Search API with appropriate credentials and endpoint. Test with a sample email requesting a book recommendation. Adjust the Generate Email Content node for custom email templates if needed. Ensure the Send Email node is linked to a valid email service. Requirements Email service API credentials (e.g., Gmail, SMTP) Ollama LLM API access Book Search API credentials Customizing This Workflow Modify the Analyze Email with Ollama node to refine intent extraction for specific genres. Adjust the Book Search API query to target different book databases. Customize the Generate Email Content node to include additional details like author bios.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 🎬 Seedance Video Marketing AI Agent Description: This AI-powered marketing automation workflow takes a user prompt, researches trending topics, generates a compelling short-form video prompt, and sends it to the Seedance API via Wavespeed to create a ready-to-use video ad — all from a single Telegram message. Built for marketers, founders, and content creators who want to turn trend-based ideas into visual video content without touching a video editor. For the step-by-step video tutorial guide on how to build this workflow, check out: https://youtu.be/2oZ5NhosKgo 🤖 How It Works: 📲 Telegram Trigger  Kick off the workflow by messaging a short prompt (e.g., “Create a 5-second IG ad for my new perfume”) via Telegram. 📈 Trend Research with Perplexity AI (Sonar Pro)  An AI agent scans the latest 14-day trends and selects the top marketing angle based on the product/niche input. 🧠 Video Prompt Engineer (ChatGPT)  Crafts a concise, visually rich video generation prompt — optimized for Seedance — based on the trend insight and product. 🎥 Video Generation (Wavespeed + Seedance API)  Sends the AI-generated prompt to Seedance via Wavespeed to generate a 5-second short-form video ad. 🔁 Status Loop & Response  The workflow checks if the video is ready. Once complete, it sends the final video output URL back to you in Telegram. 🔧 Tools Used: Telegram Trigger & Response Perplexity AI (Sonar Pro) OpenAI Seedance API (via Wavespeed) n8n HTTP Request, Wait, and Loop nodes 💡 Use Cases: Auto-generate TikTok/Instagram ads from current trends Scale creative content generation with zero design work Plug into your marketing chatbot or campaign assistant Use trends as visual inspiration for ad creatives If you like the build, check out my channel and consider subscribing: https://www.youtube.com/@Automatewithmarc