by Alex
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.
by Derek Cheung
Use case This workflow enables a Telegram bot that can: Accept speech input in one of 55 supported languages Automatically detect the language spoken and translate the speech to another language Responds back with the translated speech output. This allows users to communicate across language barriers by simply speaking to the bot, which will handle the translation seamlessly. How does it work? Translation In the translation step the workflow converts the user's speech input to text and detects the language of the input text. If it's English, it will translate to French. If it's French, it will translate to English. To change the default translation languages, you can update the prompt in the AI node. Output In the output step, we provide the translated text output back to the user and speech output is generated in the translated language. Setup steps Obtain Telegram API Token Start a chat with the BotFather. Enter /newbot and reply with your new bot's display name and username. Copy the bot token and use it in the Telegram node credentials in n8n. Update the Settings node to customize the desired languages Activate the flow Full list of supported languages All supported languages:
by Xiaoyuan Zhang
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.
by Jimleuk
This n8n template demonstrates how to calculate the evaluation metric "Relevance" which in this scenario, measures the relevance of the agent's response to the user's question. 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_relevance.py How it works This evaluation works best for Q&A agents. For our scoring, we analyse the agent's response and ask another AI to generate a question from it. This generated question is then compared to the original question using cosine similarity. A high score indicates relevance and the agent's successful ability to answer the question whereas a low score means agent may have added too much irrelevant info, went off script or hallucinated. 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
by Yang
🧾 What this workflow does This workflow takes a reference ad image and brand website, then uses GPT-4, LangChain, and Dumpling AI to generate 10 high-quality image variations for ad testing. These image variations are visually consistent but subtly different in background, mood, lighting, and tone — perfect for performance testing on platforms like Meta Ads or TikTok. 👤 Who is this for DTC marketers and brand designers testing ad creatives Creative teams automating visual experimentation Content agencies using AI for fast ad mockups Performance marketers running multivariate testing ⚙️ How to set up ✅ Requirements You’ll need the following tools set up in n8n: Google Drive (OAuth2 credential) Google Sheets (OAuth2 credential) OpenAI API (for GPT-4 or GPT-4o) Dumpling AI API (via HTTP header authentication) 🛠️ Steps to configure Google Sheet Setup Create a sheet with one column: Image URL Update the Sheet ID and tab name in the final Google Sheets node. Drive Setup Create a folder in Google Drive for storing the reference image. Replace the folderId in the “Upload Ad Image to Google Drive” node. Dumpling AI API Key Use n8n’s credential manager (HTTP Header Auth) — do not hardcode the key. OpenAI API Key Required for both image description and LangChain agent prompt generation. Form Inputs Required Brand Name Brand Website Ad Image (upload field) 🧠 How it works A user submits the brand name, website, and a reference ad image through a form. The image is uploaded to Google Drive. GPT-4o describes the image’s visual style (e.g., mood, lighting, composition). GPT-4 analyzes the brand’s website to define its visual aesthetic. A LangChain agent uses both analyses to create 10 tightly scoped variation prompts. Dumpling AI generates a new image for each prompt using its “FLUX.1-pro” model. Each new image’s link is logged into Google Sheets. 🛠️ How to customize 🧪 Change prompt logic to experiment with different variations (e.g., theme, season). 🎨 Switch image model in Dumpling AI to one that supports your desired style. 🔗 Log additional metadata (prompt, timestamp) to Google Sheets. 📤 Connect output images to Airtable, Notion, or a review tool like Figma. 🎯 Modify GPT system message to reflect a different tone or brand strategy. This workflow gives creative teams and marketers an instant, AI-powered ad image testing system — built on real brand visuals, not generic stock content.
by Leonard
Unlock AI-Driven Research with Jina AI (No API Key Needed!) Following the success of Open Deep Research 1.0, we are excited to introduce an improved and fully free version: AI-Powered Research with Jina AI Deep Search. This workflow leverages Jina AI’s Deep Search API, a free and powerful AI research tool that requires no API key. It automates querying, analyzing, and formatting research reports, making AI-driven research accessible to everyone. Key Features No API Keys Required** - Start researching instantly without setup hassle. Automated Deep Search* - Uses Jina AI to fetch *relevant and high-quality information**. Structured AI Reports** - Generates clear, well-formatted research documents in markdown. Flexible and Customizable* - Modify the workflow to fit *your specific research needs**. Ideal for Researchers, Writers & Students** - Speed up your research workflow. Use Cases This workflow is particularly useful for: Researchers** - Quickly gather and summarize academic papers, online sources, and deep web content. Writers & Journalists** - Automate background research for articles, essays, and investigative reports. Students & Educators** - Generate structured reports for assignments, literature reviews, or presentations. Content Creators** - Find reliable sources for blog posts, videos, or social media content. Data Analysts** - Retrieve contextual insights from various online sources for reports and analysis. How It Works The user submits a research query via chat. The workflow sends the query to Jina AI’s Deep Search API. The AI processes and generates a well-structured research report. A code node formats the response into clean markdown. The final output is a structured, easy-to-read AI-generated report. Pre-Conditions & Requirements An n8n instance (self-hosted or cloud). No API keys needed** – Jina AI Deep Search is completely free. Basic knowledge of n8n workflow automation is recommended for customization. Customization Options This workflow is fully modular, allowing users to: Modify the query prompt to refine the research focus. Adjust the report formatting to match personal or professional needs. Expand the workflow by adding additional AI tools or data sources. Integrate it with other workflows in n8n to enhance automation. Users are free to connect it with other workflows, add custom nodes, or tweak existing configurations. Getting Started Setup Time: Less than 5 minutes Import the workflow into n8n. Run the workflow and input a research topic. Receive a fully formatted AI-generated research report. Try It Now! Start your AI-powered research with Jina AI Deep Search today! Get the workflow on n8n.io
by Angel Menendez
Who is this for? This workflow is for professionals and teams who want to automate LinkedIn message replies with intelligent, human-like responses — without losing control over tone or accuracy. Ideal for founders, sales teams, DevRel, or community managers handling high-volume inbound messages. What problem is this workflow solving? Responding to every LinkedIn message manually is slow and inconsistent. Basic AI bots generate replies without context or nuance. This subworkflow solves both problems by using structured message routing from Notion and profile insights from UniPile to craft smart, context-aware responses. What this workflow does This workflow takes the sender’s message and profile (from LinkedIn Auto Message Router with Request Detection) and references your centralized Notion database of message types. It uses that to either match the message to a known response or generate a new one using OpenAI's GPT model — all while following professional tone guidelines. This is the third workflow in a 3-part automation system: Receives data from LinkedIn Auto Message Router with Request Detection Uses UniPile LinkedIn Profile Lookup Subworkflow to enrich responses based on follower count or org data Example Use Case If a message comes from someone with low reach (e.g., under 1,000 followers), the AI politely deflects a meeting request. If an influencer reaches out, the AI immediately offers a booking link. Your team controls this logic by updating the Notion database — no edits to the workflow required. Setup Connect this workflow as a subworkflow in your router or Slack approval flow Store your Notion API key and database ID in n8n Provide the following parent inputs: message – The LinkedIn message text sender – Name of the sender chatid – Session ID (optional for memory) linkedinprofile – Enriched array with LinkedIn context (follower count, connection info, etc.) Add your preferred AI model credentials (supports OpenAI, Gemini, or Ollama) Optional: Customize system prompt to better match your brand voice How to customize this workflow to your needs Update the Notion schema to include industry-specific categories or actions Change the AI tone (e.g., humorous, more corporate, etc.) Add conditional logic for auto-sending messages without Slack approval Extend to support multiple platforms (e.g., email, X/Twitter, Instagram DMs)
by Onur
Effortless Task Management: Create Todoist Tasks Directly from Telegram with AI This n8n workflow empowers you to seamlessly manage your tasks by creating Todoist entries directly from Telegram, using the power of AI. Simply send a voice or text message to your Telegram bot, and this workflow will transform it into actionable tasks in your Todoist account. Who is this for? Busy professionals** who need a quick and easy way to capture tasks on the go. Students** looking to streamline their assignments and project management. Anyone** who wants to leverage AI for effortless task management. What Problem Does it Solve? This workflow eliminates the need to manually enter tasks into Todoist. It automates the process of capturing, organizing, and prioritizing tasks, saving you time and effort. What are the Benefits? Seamless Integration:** Connect your Telegram and Todoist accounts for a frictionless workflow. AI-Powered Task Breakdown:** LLM AI intelligently analyzes your messages and breaks them down into manageable sub-tasks. Voice-to-Task:** Create tasks with voice messages for hands-free convenience. Increased Productivity:** Capture and organize tasks quickly, keeping you focused and productive. Accessibility:** Access your tasks from anywhere with Todoist's mobile app and Google extension. How it Works Send a message: Send a voice or text message describing your task to your Telegram bot. AI analysis: The workflow uses an LLM (OpenAI Chat Model) to analyze your message and break it down into sub-tasks. Task creation: The workflow creates tasks in your Todoist account based on the AI's analysis. Notification: You receive a Telegram notification with a link to your newly created tasks in Todoist. Nodes in the Workflow Telegram Trigger:** Listens for incoming messages on Telegram. Switch:** Routes messages based on their type (voice or text). Telegram:** Fetches voice messages from Telegram. OpenAI:** Transcribes voice messages to text using OpenAI's Whisper API. Edit Fields:** Prepares the text for the LLM. Basic LLM Chain:** Analyzes messages and generates sub-tasks using OpenAI's GPT model. Structured Output Parser:** Extracts sub-tasks from the LLM's response. Todoist:** Creates tasks in your Todoist account. Telegram:** Sends a notification with a link to your Todoist tasks. Requirements Active n8n instance. Telegram account with a bot. Todoist account. OpenAI API key. Setup Information Import the workflow JSON into your n8n instance. Configure the Telegram Trigger node with your bot token. Set up the OpenAI credentials with your API key. Connect your Todoist account in the Todoist node. Customize the LLM prompt (optional) to fine-tune task creation. Additional Tips Explore Todoist's features to further organize and manage your tasks. Experiment with different LLM prompts to optimize task breakdown. Use n8n's features to automate other aspects of your workflow. This workflow combines the convenience of Telegram with the power of AI and Todoist to provide a seamless task management experience. Start managing your tasks effortlessly today!
by Don Jayamaha Jr
Meet your AI-powered crypto data analyst—fully integrated with CoinMarketCap APIs. This workflow acts as the supervisor agent for a multi-agent architecture built in n8n, connecting three powerful sub-agents to extract real-time insights from centralized and decentralized markets. It’s the ultimate tool for crypto traders, analysts, developers, and researchers who need strategic multi-source intelligence—all through Telegram. This workflow requires 3 sub-agent templates to function correctly. See below. 🔌 Required Sub-Workflows (Install First) CoinMarketCap Crypto Agent Tool → Token prices, metadata, conversions, listings CoinMarketCap Exchange & Community Agent Tool → Exchange info, token holdings, Fear & Greed index CoinMarketCap DEXScan Agent Tool → DEX trading pairs, liquidity, OHLCV data Download all from my Creator Profile: https://n8n.io/creators/don-the-gem-dealer/ What Makes This Workflow Special? This is not just another API wrapper—it’s an intelligent routing agent powered by GPT-4o-mini, capable of: Understanding complex user queries Choosing the appropriate tool workflow Structuring the API request Executing sub-workflows Formatting the output Returning insights via Telegram It connects three domains of market data: Cryptocurrencies (CEX)** Exchanges & Sentiment** DEX trading data** 🔍 What You Can Do 💰 Token Intelligence Get token metadata, price, volume, supply Compare rankings and conversions 🏦 Exchange Insights View assets held by exchanges Track the CMC 100 Index and Fear & Greed Score 🌐 DEX Market Analysis Analyze pair quotes, historical OHLCV, live trades Discover the top DEXs by volume across blockchains ✅ Example Questions to Ask “What’s the market cap of Ethereum today?” “Show liquidity and volume for SOL/USDT on Solana” “Get token holdings for Binance” “Compare BTC price on Uniswap vs Binance” “What’s the Fear & Greed index right now?” 🛠️ Setup Instructions Create Telegram Bot Use @BotFather to get your bot token. Get CoinMarketCap API Key Apply here: https://coinmarketcap.com/api/ Install Sub-Agent Templates Required: Crypto Agent Tool Exchange & Community Tool DEXScan Tool Configure Credentials in n8n Add both Telegram and CoinMarketCap keys as HTTP Header Auth. Deploy & Test Ask your Telegram bot: “Top 10 tokens by 24h volume” or “Convert 5 ETH to USD” Workflow Architecture AI Brain**: GPT-4o-mini Memory**: Windowed buffer memory via sessionId Tool Agents**: toolWorkflow() → routes requests to the appropriate sub-agent Executes real-time API queries and returns structured output Included Sticky Notes System Overview** Error Handling Guide (200, 400, 401, 429, 500)** Step-by-Step Usage Instructions** Prompt Examples + API Docs** Legal & Licensing Notes** Your crypto insights—smarter, faster, and all in one Telegram message.
by HoangSP
Medical Q&A Chatbot for Urology using RAG with Pinecone and GPT-4o This template provides an AI-powered Q&A assistant for the Urology domain using Retrieval-Augmented Generation (RAG). It uses Pinecone for vector search and GPT-4o for conversational responses. 🧠 Use Case This chatbot is designed for clinics or medical pages that want to automate question answering for Urology-related conditions. It uses a vector store of domain knowledge to return verified responses. 🔧 Requirements ✅ OpenAI API key (GPT-4o or GPT-4o-mini) ✅ Pinecone account with an active index ✅ Verified Urology documents embedded into Pinecone ⚙️ Setup Instructions Create a Pinecone vector index and connect it using the Pinecone credentials node. Upload Urology-related documents to embed using the Create Embeddings for Urology Docs node. Customize the chatbot system message to reflect your medical specialty. Deploy this chatbot on your website or link it with Telegram via the chat trigger node. 🛠️ Components chatTrigger: Listens for user messages and starts the workflow. Medical AI Agent: GPT-based agent guided by domain-specific instructions. RAG Tool Vector Store: Fetches relevant documents from Pinecone using vector search. Memory Buffer: Maintains conversation context. Create Embeddings for Urology Docs: Encodes documents into vector format. 📝 Customization You can replace the knowledge base with any other medical domain by: Updating the documents stored in Pinecone. Modifying the system prompt in the AI Agent node. 📣 CTA This chatbot is ideal for clinics, medical consultants, or educational websites wanting a reliable AI assistant in Urology.
by Yaron Been
Scrape Indeed Job Listings for Hiring Signals Using Bright Data and LLMs How the flow runs Fill the form with job position you're hunting for. Bright data's scraper will scrape Indeed based on your requirments. Workflow waits for the snapshot. Data returns as JSON. Jobs append to Google Sheets. Each row goes to an LLM to analyze if you're a good fit for the job (based on your prompts). The LLMswrites YES or NO next to each job opportunity, helping you find job posts that are relevant to you. What you need Google Sheets with our template. Bright Data dataset and API key. OpenAI key for GPT‑4o mini (or any other LLM). n8n with required nodes. Form fields To Fill Job Location** – city or region. Keyword** – role or skills. Country** – two‑letter code. Setup steps Copy the sheet template link. Import the JSON workflow. Add your credentials in nodes. Test the form manually. Add a schedule if desired. Bright Data filter example [ { "country": "US", "domain": "indeed.com", "keyword_search": "Growth Marketer", "location": "Miami", "date_posted": "Last 24 hours" } ] Tips -Choose Last 24 hours often. -Increase wait time for big snapshots. -Narrow keywords to save credits. **Need help? **Email me anytime: Yaron@nofluff.online YouTube: @YaronBeen LinkedIn: https://www.linkedin.com/in/yaronbeen/ Bright Data Docs: https://docs.brightdata.com/introduction
by Leonardo Grigorio
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