Workflow Templates

This n8n template demonstrates how to calculate the evaluation metric "Similarity" which in this scenario, measures the consistency of the agent. The scoring approach is adapted from the open-source evaluations project RAGAS and you can see the source here https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_similarity.py How it works This evaluation works best where questions are close-ended or about facts where the answer can have little to no deviation. For our scoring, we generate embeddings for both the AI's response and ground truth and calculate the cosine similarity between them. A high score indicates LLM consistency with expected results whereas a low score could signal model hallucination. Requirements n8n version 1.94+ Check out this Google Sheet for a sample data https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing

Transform your BeyondPresence video agent conversations into comprehensive insights by automatically analyzing each call with AI and organizing 35+ data points in Google Sheets. This template helps customer success, support, and training teams save 30+ minutes per call on documentation while ensuring no critical action items or insights are missed. How it works Webhook receives** completed call data from BeyondPresence including full transcript Data validation** ensures quality and adds enriched metadata (duration, time calculations) AI analysis** (GPT-4) extracts action items, sentiment, decisions, and recommendations Parse response** handles the AI output and structures it for sheets Auto-append** to Google Sheets with 35+ insights per call organized beautifully Set up steps Copy our Google Sheets template - One click! Get pre-formatted sheet: BeyondPresence Call Analytics Template Connect accounts - Add OpenAI API key and Google Sheets OAuth2 Configure webhook - Copy URL from n8n to BeyondPresence Settings → Webhooks Customize AI prompt (optional) - Adjust analysis focus for your use case Test with a call - Make a test call and watch insights appear! Setup time: 5-10 minutes Requirements: BeyondPresence account, OpenAI API key, Google account

This n8n workflow leverages a Telegram Message Trigger to activate an intelligent AI Agent capable of processing both text and voice messages. When a user sends a message in text or in voice format, the workflow captures and transcribes it (if necessary), then passes it to the AI Agent for understanding and response generation. To enhance user experience, the bot also displays a typing indicator while processing requests, simulating a natural, human-like interaction. Key Features Multi-Modal Input: Supports both text messages and voice notes from users. Real-Time Interaction: Shows a “typing…” action in Telegram while the AI processes the input. AI Agent Integration: Provides intelligent, context-aware, and conversational responses. Seamless Feedback Loop: Replies are sent directly back to the user within Telegram for smooth interaction. How It Works The workflow triggers whenever a message or voice note is received on Telegram. If the input is a voice note, the workflow transcribes it into text. The text input is sent to the AI Agent for processing. While processing, the bot sends a typing indicator to the user. Once the AI generates a response, the workflow sends it back to the user in Telegram. Setup Instructions Create a Telegram Bot: Use @BotFather to create a bot and obtain your bot token. Configure n8n Credentials: Add Telegram API credentials in n8n with your bot token. Add credentials for any speech-to-text service used for voice transcription (e.g., Open AI Transcribe A Recording). Import the Workflow: Import this workflow into your n8n instance. Update all credential nodes to use your Telegram and transcription service credentials. Set Webhook URLs: Ensure Telegram webhook is set properly for your bot to receive messages. Make sure your n8n instance is publicly accessible for Telegram callbacks. Test the Workflow: Send text messages and voice notes to your Telegram bot and observe the AI responses. Customization Guidance Add new message handlers: Extend the workflow to handle additional message types (images, documents, etc.). Improve transcription: Swap or add speech-to-text services for better accuracy or language support. Enhance AI Agent: Customize prompts and context management to tailor the AI’s personality and responses. AI Model Flexibility: Swap between different AI models (e.g., GPT-4, Claude, or custom LLMs) based on task type, cost, or performance preferences. Tool-Based Control: Add custom tools to the AI Agent such as calendar access, Notion, Google Sheets, web search, database queries, or custom APIs—allowing for dynamic, multi-functional agents Security and Implementation Notes The Telegram node manages message reception and sending but does not directly handle AI processing. Voice transcription requires integration with external APIs; secure those credentials in n8n and monitor usage. To simulate typing, the workflow uses Telegram’s “sendChatAction” API method, providing users with feedback that the bot is processing. Ensure your AI API keys and Telegram tokens are securely stored in n8n credentials and not exposed in workflows or logs. Benefits Handles natural conversational inputs with text or voice. Provides a smooth, engaging user experience via typing indicators. Easy integration of advanced AI conversational agents with Telegram. Flexible for personal assistants, helpdesks, or interactive chatbots.

This workflow demonstrates a simple way to run evals on a set of test cases stored in a Google Sheet. The example we are using comes from an info extraction task dataset, where we tested 6 different LLMs on 18 different test cases. You can see our sample data in this spreadsheet here to get started. Once you have this working for our dataset, you can plug in your own test cases matching different LLMs to see how it works with your own data. How it works: It loads test cases from Google Sheets. For each row in our Google Sheet, it grabs the source document, converting it to text. Our "LLM judge" passes the input/output of each LLM to GPT-4.1 to evaluate each test case (Pass/Fail + Reason). It logs the outcome to a Google Sheet. A 0.5s pause between each request gets around OpenAI's API rate limits. Set up steps: Add your credentials for Google Sheets, Google Drive, and OpenRouter. Make a copy of the original data spreadsheet so that you can edit it yourself. You will need to plug your version in the Update Results node to see the spreadsheet update on each run of the loop.

Who is this for? This workflow is ideal for SEO specialists, web designers, and digital marketers who want to quickly draft effective landing page layouts by referencing established competitors. It suits users who need a fast, structured starting point for web design while ensuring competitive relevance. What problem is this workflow solving? / Use case Designing a high-converting landing page from scratch can be time-consuming. This workflow automates the process of analyzing a competitor’s website, identifying essential sections, and producing a tailored layout—helping users save time and improve their website’s effectiveness. What this workflow does The workflow fetches and analyzes your chosen competitor’s landing page, using web scraping and structure-detection nodes in n8n. It identifies primary sections like hero banners, service highlights, testimonials, and contact forms, and then generates a simplified, customizable layout suitable for wireframing or initial design. Setup Prepare your unique services and target audience profile for customization later. Gather the competitor’s landing page URL you wish to analyze. Run the workflow, inputting your competitor’s URL when prompted. How to customize this workflow to your needs After generating the initial layout, adapt section names and content blocks to highlight your services and brand messaging. Add or remove sections based on your objectives and audience insights. Integrate additional nodes for richer analysis, such as keyword extraction or design pattern detection, to tailor the output further.

🔁 What This Workflow Does This automation fetches daily AI-related articles from trusted RSS feeds, summarizes them using OpenAI (GPT), and generates a ready-to-post LinkedIn update in your writing style. It then emails the post to you every morning for review and publishing. High-Level Steps: Triggers every morning via Cron. Fetches latest AI news from multiple RSS sources. Filters recent articles (last 24 hrs). Summarizes each article using OpenAI (ChatGPT). Generates a LinkedIn-style post using your tone. Sends the post to your Gmail for review. ⚙️ Setup Steps Estimated setup time: 15–30 minutes You’ll need: OpenAI API key Gmail account connected in n8n RSS feed URLs (defaults are provided) Add your email in the Gmail node to receive daily posts. Add your tone/style prompt in the ChatGPT nodes (instructions inside workflow).

Automate LinkedIn engagement without sounding like a bot. This workflow: 🌍 Detects language & tone (German / English) 👍 Chooses the right reaction (like / celebrate / support …) 🗣 Generates a personalised comment in your voice and mentions the author 📲 Optional Telegram review – approve ✅ or regenerate ❌ before posting 💸 Runs on cost-efficient GPT-4o mini or Claude 3.5 Haiku ☁️ Publishes comment + reaction via the Unipile API Setup (≈ 15-30 min) Unipile – connect LinkedIn → copy account_id, dsn, then create an Access-Token (X-API-KEY). Telegram (optional) – create a bot, add a credential named YOUR TELEGRAM ACCOUNT. OpenAI / Anthropic – add your API key and keep one LLM node (delete the other). Open the “Defining guardrails” node and replace the credential placeholders. (Optional) Tweak role, comment_length and openers_example_1-3 for your brand voice. Security: no live keys included – all secrets are placeholders. Best for: solopreneurs, marketing teams, personal-branding consultants.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How It Works This template orchestrates a multi-step workflow that constructs a comprehensive four-zone automation matrix—Green, Yellow, Red, and White—grounded in the Human Agency Scale (HAS). When a user sends a job title via Telegram, the workflow routes both text and voice messages appropriately. Voice messages are transcribed via OpenAI's Whisper, while text inputs bypass transcription. Both streams merge into a single data flow. The AI Agent node, powered by GPT-4, analyzes the user's profession and core tasks. It also leverages live context by calling the Tavily search tool, ensuring the analysis incorporates up-to-date information. After the evaluation, the workflow formats and returns the completed matrix, with detailed task examples and rationales for each zone, back to the user via Telegram. Setup Instructions Create an OpenAI credential in n8n (model: GPT-4.1 mini). Add a Tavily credential with your API key (FREE plan available). Configure a Telegram Bot credential: API bot token. Import this JSON as a new workflow in n8n and map credentials in each node. Activate the workflow; test by sending sample job titles; adjust node timeouts and webhook settings as needed. Requirements n8n v1.0.0 or higher Active OpenAI API key (GPT-4.1 mini access) Tavily API key for web context search Telegram Bot token with correctly configured webhook Stable internet connectivity Audience & Problem This template is designed for consultants, HR professionals, and analysts who need a scalable, standardized approach to evaluate which routine tasks in a given profession can be automated, which require human oversight, and which should remain manual to preserve strategic judgment, creativity, and expertise.

Description This workflow creates a sophisticated bilingual dictionary that provides literary-style definitions and examples for English and German words. The system automatically detects the input language, generates comprehensive definitions in Chinese, creates three literary-style example sentences with translations, and stores everything in a Supabase database for future reference. Who Is This For? Language Learners & Students: Perfect for those studying English or German who want to understand words in literary contexts with Chinese translations. Writers & Content Creators: Ideal for bilingual writers working with English, German, and Chinese who need rich, literary examples for their work. Educators & Translators: Excellent tool for language teachers and professional translators who need comprehensive word definitions with contextual examples. Literary Enthusiasts: Great for readers of literature who encounter unfamiliar words and want to understand their poetic or literary usage. What Problem Does This Workflow Solve? Traditional dictionaries often provide basic definitions without literary context or cross-language examples. This workflow addresses several key challenges: Limited Literary Context: Most dictionaries lack poetic, expressive, or literary-style examples that help understand how words are used in sophisticated writing. Cross-Language Learning: Provides seamless translation between English/German and Chinese with culturally appropriate examples. Data Persistence: Automatically saves all lookups to a database, creating a personalized vocabulary collection over time. API Accessibility: Provides a clean webhook interface that can be integrated into apps, websites, or other tools. How It Works Main Dictionary Lookup Flow Input Processing: Receives a word via webhook POST request and automatically detects if it's English or German AI Analysis: Uses OpenAI GPT-4o-mini to generate comprehensive definitions with literary context Response Formatting: Processes the AI response to extract structured data (word, meaning, examples) Quality Control: Validates the response and handles unclear or invalid inputs gracefully Database Storage: Saves the word, Chinese meaning, and examples to Supabase for future reference API Response: Returns formatted JSON with the complete dictionary entry Data Storage Flow Parallel Processing: Simultaneously returns the dictionary data to the user and saves it to the database Structured Storage: Organizes data in Supabase with fields for words, Chinese meanings, and example arrays Success Confirmation: Provides confirmation when data is successfully stored Setup Instructions Prerequisites & Accounts You'll need accounts and API access for: n8n (Cloud or self-hosted) OpenAI (API key required) Supabase (Database and API credentials) Webhook Configuration The workflow uses two webhook endpoints with the same path for different operations Note the webhook URL provided by n8n for API integration Test the webhook endpoints to ensure they're accessible approach Customization Options Extend to support additional input languages by modifying the AI prompt Add support for other target languages beyond Chinese Customize the literary style for different cultural contexts This workflow transforms simple word lookups into rich, contextual learning experiences while building a personalized vocabulary database over time.

How it works Automates the retrieval of Calvin and Hobbes daily comics. Extracts the comic image URL from the website. Translates comic dialogues to English and Korean. Posts the comic and translations to Discord daily. Set up steps Estimated setup time: ~10-15 minutes. Use a Schedule Trigger to automate the workflow at 9 AM daily. Add nodes for parameter setup, HTTP request, data extraction, and integration with Discord. Add detailed notes to each node in the workflow for easy understanding.

Youtube Video This n8n workflow is designed to assist YouTube content creators in identifying trending topics within a specific niche. By leveraging YouTube's search and data APIs, it gathers and analyzes video performance metrics from the past two days to provide insights into what content is gaining traction. Here's how the workflow operates: Trigger Setup: The workflow begins when a user sends a query through the chat_message_received node. If no niche is provided, the AI prompts the user to select or input one. AI Agent (Language Model): The central node utilizes a GPT-based AI agent to: Understand the user's niche or content preferences. Generate tailored search terms related to the niche. Process YouTube API responses and summarize trends using insights such as common themes, tags, and audience engagement metrics (views, likes, and comments). YouTube Search: The youtube_search node runs a secondary workflow to query YouTube for relevant videos published within the last two days. It retrieves basic video data such as video IDs, relevance scores, and publication dates. Video Details Retrieval: The workflow fetches additional details for each video: Video Snippet: Metadata like title, description, and tags. Video Statistics: Metrics such as views, likes, and comments. Content Details: Video duration, ensuring only content longer than 3 minutes and 30 seconds is analyzed. Data Processing: Video metadata is cleaned, sanitized, and stored in memory. Tags, titles, and descriptions are analyzed to identify patterns and trends across multiple videos. Output: The workflow compiles insights and presents them to the user, highlighting: The most common themes or patterns within the niche. URLs to trending videos and their respective channels. Engagement statistics, helping the user understand the popularity of the content. Key Notes for Setup: API Keys**: Ensure valid YouTube API credentials are configured in the get_videos, find_video_snippet, find_video_statistics, and find_video_data nodes. Memory Buffer**: The window_buffer_memory node ensures the AI agent retains context during analysis, enhancing the quality of the generated insights. Search Term Customization**: The AI agent dynamically creates search terms based on the user’s niche to improve search precision. Use Case: This workflow is ideal for YouTubers or marketers seeking data-driven inspiration for creating content that aligns with current trends, maximizing the potential to engage their audience. Example Output: For the niche "digital marketing": Trending Topic: Videos about "mental triggers" and "psychological marketing." Tags: "SEO," "Conversion Rates," "Social Proof." Engagement: Videos with over 200K views and high likes/comment ratios are leading trends. Video links: https://www.youtube.com/watch?v=video_id1 https://www.youtube.com/watch?v=video_id2

How it works • Allows users to manage their Google Calendar via WhatsApp using natural language • Handles event creation, updates, deletions, availability checks, and agenda overviews • AI agent interprets the user’s message and triggers the appropriate calendar action • Responses are sent back to the user via WhatsApp, with confirmation or schedule info Set up steps • Set up a WhatsApp Business Cloud account and configure your webhook • Connect your Google Calendar using n8n credentials • Deploy OpenAI API key for natural language understanding • Link each calendar action (create, update, delete, search) to the TimePilot agent • Customize confirmation messages and automate reply formatting Note: More detailed configuration and custom logic are described inside sticky notes within the workflow.