Workflow Templates

This n8n workflow automates the discovery, enrichment, and comparative analysis of startups from the Crunchbase dataset via Bright Data, enhanced with AI, and exports structured results to Google Sheets. 🚀 What It Does Receives a keyword from the user that describes the area of interest — such as an industry, sector, technology, or trend (e.g., "AI in healthcare", "carbon capture", "edtech"). This keyword is used to filter relevant startups from the Crunchbase dataset via Bright Data. Fetches data from Bright Data's Crunchbase snapshot API. Extracts and cleans key fields from the JSON response. Sorts startups by most recent founding date. Selects the top 10 most recent companies. Sends these 10 companies to Google Gemini AI for comparative analysis. Embeds the AI-generated summary into the final export. Appends results to a Google Sheet for tracking and reporting. 🛠️ Step-by-Step Setup Get user keyword input from a form. Use 3 Bright Data requests: Start snapshot. Poll snapshot status until ready. Fetch snapshot data in JSON format. Use a Python Code node to: Parse and sort companies by founded_date. Clean and standardize data fields. Pass the top 10 companies into Gemini AI for comparative insight. Merge the AI output back with company data. Send everything to Google Sheets. 🧠 How It Works Snapshot Control: Polls every few seconds until the Bright Data snapshot is complete. Code Cleanup: Ensures consistent structure and formatting across all records. Comparative AI Analysis: Gemini compares all 10 companies at once and returns a unified analysis. Merging Output: AI analysis is merged into the first company’s record (to avoid duplication), while all 10 are exported. 📤 Google Sheet Output Each row includes: name, founded, about, num_employees, type, ipo_status, full_description, social_media_links, address, website, funding_total, num_investors, lead_investors, founders, products_and_services, monthly_visits, crunchbase_link, ai_analysis. AI comparative analysis summary (only once per batch – attached to the first company). All fields from above customizible through the python code (you can add additional ones from Bright Data output). 🔐 Required Credentials Bright Data* – Replace *YOUR_API_KEY** in 3 HTTP Request nodes. Google Gemini API** – For AI analysis. Google Sheets OAuth2** – For spreadsheet export. ⚠️ Notes AI output is shared once per batch of 10 companies, attached to the first company entry. You can configure the limit of batch size in the first "Code" node.

A short-term technical analysis agent for 15-minute candles on Binance Spot Market pairs. Calculates and interprets key trading indicators (RSI, MACD, BBANDS, ADX, SMA/EMA) and returns structured summaries, optimized for Telegram or downstream AI trading agents. This tool is designed to be triggered by another workflow (such as the Binance SM Financial Analyst Tool or Binance Quant AI Agent) and is not intended for standalone use. 🔧 Key Features ⏱️ Uses 15-minute kline data (last 100 candles) 📈 Calculates: RSI, MACD, Bollinger Bands, SMA/EMA, ADX 🧠 Interprets numeric data using GPT-4.1-mini 📤 Outputs concise, formatted analysis like: • RSI: 72 → Overbought • MACD: Cross Up • BB: Expanding • ADX: 34 → Strong Trend 🧠 AI Agent Purpose > You are a short-term analysis tool for spotting volatility, early breakouts, and scalping setups. Used by higher agents to determine: Entry/exit precision Momentum shifts Scalping opportunities ⚙️ How it Works Triggered externally by another workflow Accepts input: { "message": "BTCUSDT", "sessionId": "123456789" } Sends POST request to backend endpoint: https://treasurium.app.n8n.cloud/webhook/15m-indicators Fetches last 100 candles and calculates indicators Passes data to GPT for interpretation Returns summary with indicator tags for human readability 🔗 Dependencies This tool is triggered by: ✅ Binance SM Financial Analyst Tool ✅ Binance Spot Market Quant AI Agent 🚀 Setup Instructions Import into your n8n instance Make sure /15m-indicators webhook is active and calculates indicators correctly Connect your OpenAI GPT-4.1-mini credentials Trigger from upstream agent with Binance symbol and session ID Ensure all external calls (to Binance + webhook) are working 🧪 Example Use Cases | Use Case | Result | | ------------------------------------- | --------------------------------------- | | Short-term trade decision for ETHUSDT | Receives 15m signal indicators summary | | Input from Financial Analyst Tool | Returns real-time volatility snapshot | | Telegram bot asks for “DOGE update” | Returns momentum indicators in 15m view | 🎥 Watch Tutorial: 🧾 Licensing & Attribution © 2025 Treasurium Capital Limited Company Architecture, prompts, and trade report structure are IP-protected. No unauthorized rebranding or resale permitted. 🔗 For support: Don Jayamaha – LinkedIn

Generate Summaries from Uploaded Files using OpenAI Assistants API 📑 Overview Upload a document (PDF, DOCX, PPTX, TXT, CSV, JSON, or Markdown) and receive an AI-generated summary containing: title** – 5-10 words summary** – 1-2 sentences bullets** – 3-5 key points tags** – 3-6 short keywords The workflow: Stores the file in OpenAI. Runs an Assistant with File Search and Code Interpreter enabled. Polls until the run finishes. Retrieves the summary JSON. ✅ Prerequisites OpenAI Assistant Create one at <https://platform.openai.com/assistants> Enable File Search and Code Interpreter Note: The assistant ID starts with asst_ OpenAI API credential setup in n8n Go to Credentials → New → HTTP Header Auth Header name: Authorization Value: Bearer YOUR-OPENAI-API-KEY (replace YOUR-OPENAI-API-KEY with your OpenAI API secret key for your assistant, starts with sk-) Name it: openAIApiHeader 🔧 Setup Import the workflow JSON. When n8n prompts for a credential, choose openAIApiHeader for every HTTP Request node. Open Run Assistant → Body and replace "assistant_id": "REPLACE_WITH_YOUR_ASSISTANT_ID" with your real ID (starts with asst_…). Save. 🚀 How it works | # | Node | Purpose | |---|------|---------| | 1 | On form submission | User uploads a file (File). | | 2 | Upload File | POST /v1/files (multipart) → returns file_id. | | 3 | Create Thread | Creates a thread and attaches the uploaded file. | | 4 | Run Assistant | Starts the run using your assistant_id. | | 5 | Poll Run Status → Wait 2 s → IF | Loops until status = completed. | | 6 | Fetch Summary | GET /v1/threads/{thread_id}/messages → summary JSON. | 🖌️ Customisation ideas Edit the user prompt in Create Thread to change summary length, tone, or language. Add an HTTP Response node after Fetch Summary to return plaintext to the uploader. Replace the polling loop with OpenAI’s forthcoming wait-for-run endpoint when available. No community nodes required. Works on any n8n Cloud plan (Starter, Pro, Enterprise) or self-hosted Community Edition.

Who is this for? BlueSky users looking to automate the publication of new posts based on new items from a RSS feed. What this workflow does This will create a BlueSky post with each new RSS feed item, including the feed title, post image, link and content (up to 200 characters) Setup You'll need to generate a BlueSky app password Configure your feed URL in the first node Configure your credentials in the second node How to customize this workflow to your needs You can modify the message posted in the `Create post node, changing the JSON text` value, in case you want to include only the feed item title instead of the content. If you RSS feed doesn't provide an image, you can define a static one on the `Download image` node.

🎵 TikTok Post Scraper via Keywords | Bright Data + Sheets Integration 📝 Workflow Description Automatically scrapes TikTok posts based on keyword search using Bright Data API and stores comprehensive data in Google Sheets for analysis and monitoring. 🔄 How It Works This workflow operates through a simple, automated process: Keyword Input:** User submits search keywords through a web form Data Scraping:** Bright Data API searches TikTok for posts matching the keywords Processing Loop:** Monitors scraping progress and waits for completion Data Storage:** Automatically saves all extracted data to Google Sheets Result Delivery:** Provides comprehensive post data including metrics, user info, and media URLs ⏱️ Setup Information Estimated Setup Time: 10-15 minutes This includes importing the workflow, configuring credentials, and testing the integration. Most of the process is automated once properly configured. ✨ Key Features 📝 Keyword-Based Search Search TikTok posts using specific keywords 📊 Comprehensive Data Extraction Captures post metrics, user profiles, and media URLs 📋 Google Sheets Integration Automatically organizes data in spreadsheets 🔄 Automated Processing Handles scraping progress monitoring 🛡️ Reliable Scraping Uses Bright Data's professional infrastructure ⚡ Real-time Updates Live status monitoring and data processing 📊 Data Extracted | Field | Description | Example | |-------|-------------|---------| | url | TikTok post URL | https://www.tiktok.com/@user/video/123456 | | post_id | Unique post identifier | 7234567890123456789 | | description | Post caption/description | Check out this amazing content! #viral | | digg_count | Number of likes | 15400 | | share_count | Number of shares | 892 | | comment_count | Number of comments | 1250 | | play_count | Number of views | 125000 | | profile_username | Creator's username | @creativity_master | | profile_followers | Creator's follower count | 50000 | | hashtags | Post hashtags | #viral #trending #fyp | | create_time | Post creation timestamp | 2025-01-15T10:30:00Z | | video_url | Direct video URL | https://video.tiktok.com/tos/... | 🚀 Setup Instructions Step 1: Prerequisites n8n instance (self-hosted or cloud) Bright Data account with TikTok scraping dataset access Google account with Sheets access Basic understanding of n8n workflows Step 2: Import Workflow Copy the provided JSON workflow code In n8n: Go to Workflows → + Add workflow → Import from JSON Paste the JSON code and click Import The workflow will appear in your n8n interface Step 3: Configure Bright Data In n8n: Navigate to Credentials → + Add credential → Bright Data API Enter your Bright Data API credentials Test the connection to ensure it's working Update the workflow nodes with your dataset ID: gd_lu702nij2f790tmv9h Replace BRIGHT_DATA_API_KEY with your actual API key Step 4: Configure Google Sheets Create a new Google Sheet or use an existing one Copy the Sheet ID from the URL In n8n: Credentials → + Add credential → Google Sheets OAuth2 API Complete OAuth setup and test connection Update the Google Sheets node with your Sheet ID Ensure the sheet has a tab named "Tiktok by keyword" Step 5: Test the Workflow Activate the workflow using the toggle switch Access the form trigger URL to submit a test keyword Monitor the workflow execution in n8n Verify data appears in your Google Sheet Check that all fields are populated correctly ⚙️ Configuration Details Bright Data API Settings Dataset ID:** gd_lu702nij2f790tmv9h Discovery Type:** discover_new Search Method:** keyword Results per Input:** 2 posts per keyword Include Errors:** true Workflow Parameters Wait Time:** 1 minute between status checks Status Check:** Monitors until scraping is complete Data Format:** JSON response from Bright Data Error Handling:** Automatic retry on incomplete scraping 📋 Usage Guide Running the Workflow Access the form trigger URL provided by n8n Enter your desired keyword (e.g., "viral dance", "cooking tips") Submit the form to start the scraping process Wait for the workflow to complete (typically 2-5 minutes) Check your Google Sheet for the extracted data Best Practices Use specific, relevant keywords for better results Monitor your Bright Data usage to stay within limits Regularly backup your Google Sheets data Test with simple keywords before complex searches Review extracted data for accuracy and completeness 🔧 Troubleshooting Common Issues 🚨 Scraping Not Starting Verify Bright Data API credentials are correct Check dataset ID matches your account Ensure sufficient credits in Bright Data account 🚨 No Data in Google Sheets Confirm Google Sheets credentials are authenticated Verify sheet ID is correct Check that the "Tiktok by keyword" tab exists 🚨 Workflow Timeout Increase wait time if scraping takes longer Check Bright Data dashboard for scraping status Verify keyword produces available results 📈 Use Cases Content Research Research trending content and hashtags in your niche to inform your content strategy. Competitor Analysis Monitor competitor posts and engagement metrics to understand market trends. Influencer Discovery Find influencers and creators in specific topics or industries. Market Intelligence Gather data on trending topics, hashtags, and user engagement patterns. 🔒 Security Notes Keep your Bright Data API credentials secure Use appropriate Google Sheets sharing permissions Monitor API usage to prevent unexpected charges Regularly rotate API keys for better security Comply with TikTok's terms of service and data usage policies 🎉 Ready to Use! Your TikTok scraper is now configured and ready to extract valuable data. Start with simple keywords and gradually expand your research as you become familiar with the workflow. Need Help? Visit the n8n community forum or check the Bright Data documentation for additional support and advanced configuration options. For any questions or support, please contact: Email or fill out this form

Who is this for? This workflow is perfect for digital content creators, marketers, and social media managers who regularly create engaging short-form videos featuring inspirational or motivational quotes. While the workflow is universally applicable, it specifically highlights Thai as an example to demonstrate effective language and font integration. What problem is this workflow solving? Creating consistent and engaging multilingual video content manually, including attractive fonts and proper video formatting, is time-consuming and repetitive. Additionally, managing files, background music, and updating statuses manually can be tedious and prone to errors. What this workflow does Automatically fetches background video and music files stored on Google Drive. Randomly selects a quote (demonstrated with Thai language) and author information from Google Sheets. Dynamically combines the selected quote and author text using appealing fonts, such as the Thai font "Kanit," directly onto the video using FFmpeg on your n8n local environment. Creates visually engaging videos with a 9:16 aspect ratio, optimized for YouTube Shorts and other vertical video platforms. Automatically uploads the finalized video to YouTube. Updates the status and YouTube URL back into your Google Sheet, ensuring you have up-to-date records. Setup Requirements: This workflow requires a self-hosted n8n instance, as the execution of FFmpeg commands is not supported on n8n Cloud. Ensure FFmpeg is installed on your self-hosted environment. Google Sheets Setup: Your Google Sheet must include at least these columns: Index: (Unique identifier for each quote) Quote: (Text of the quote) Author: (Author of the quote) CreateStatus: (Track video creation status; values like 'DONE' or blank for pending) YoutubeURL: (Automatically updated after upload) To help you get started quickly, you can use this template spreadsheet. Next steps: Organize your video and music files in separate folders in Google Drive. Authenticate your Google Sheets, Google Drive, and YouTube accounts in n8n. Ensure fonts compatible with your target languages (such as Kanit for Thai) are available in your FFmpeg installation. How to customize this workflow to your needs Fonts:** Adjust font styles and sizes within the workflow's code node. Ensure the fonts you choose fully support the language you wish to use. Quote Management:** Easily add or remove quotes and authors in your Google Sheets document. Media Files:** Change or update background videos and music by modifying the files in your Google Drive folders. Video Specifications:** Customize video dimensions, text positioning, opacity, and music volume directly in the provided FFmpeg commands. Benefits of Using Localized Fonts and Quotes Utilizing fonts specific to your target language, as demonstrated with Thai, significantly increases audience engagement by making your content more relatable, shareable, and visually appealing. Ensure you select fonts that properly support the language you're targeting.

Deep Multiline Icebreaker System (AI-Powered Cold Email Personalization) Categories: Lead Generation, AI Marketing, Sales Automation This workflow creates an advanced AI-powered cold email personalization system that achieves 5-10% reply rates by generating deeply personalized multi-line icebreakers. The system scrapes comprehensive website data, analyzes multiple pages per prospect, and uses advanced AI prompting to create custom email openers that make recipients believe you've personally researched their entire business. Benefits Superior Response Rates** - Achieves 5-10% reply rates vs. 1-2% for standard cold email campaigns Deep Website Intelligence** - Scrapes and analyzes multiple pages per prospect, not just homepages Advanced AI Personalization** - Uses sophisticated prompting techniques with examples and formatting rules Complete Lead Pipeline** - From Apollo search to personalized icebreakers in Google Sheets Scalable Processing** - Handle hundreds of prospects with intelligent batching and error handling Revenue-Focused Approach** - System designed around proven $72K/month agency methodologies How It Works Apollo Lead Acquisition: Integrates directly with Apollo.io search URLs through Apify scraper Processes 500+ leads per search with comprehensive contact data Filters for prospects with both email addresses and accessible websites Multi-Page Website Scraping: Scrapes homepage to extract all internal website links Processes relative URLs and filters out external/irrelevant links Performs intelligent batching to prevent IP blocking during scraping Comprehensive Content Analysis: Converts HTML to markdown for efficient AI processing Uses GPT-4 to generate detailed abstracts of each webpage Aggregates insights from multiple pages into comprehensive prospect profiles Advanced AI Icebreaker Generation: Employs sophisticated prompting with system messages, examples, and formatting rules Uses proven icebreaker templates that reference non-obvious website details Generates personalized openers that imply deep manual research Smart Data Processing: Removes duplicate URLs and handles scraping errors gracefully Implements token limits to control AI processing costs Organizes final output in structured Google Sheets format Required Google Sheets Setup Create a Google Sheet with these exact tab and column structures: Search URLs Tab: URL - Contains Apollo.io search URLs for your target audiences Leads Tab (Output): first_name - Contact's first name last_name - Contact's last name email - Contact's email address website_url - Company website URL headline - Job title/position location - Geographic location phone_number - Contact phone (if available) multiline_icebreaker - AI-generated personalized opener Setup Instructions: Create Google Sheet with "Search URLs" and "Leads" tabs Add your Apollo search URLs to the first tab (one per row) Connect Google Sheets OAuth credentials in n8n Update the Google Sheets document ID in all sheet nodes The workflow reads from Search URLs and outputs to Leads automatically Apollo Search URL Format: Your search URLs should look like: https://app.apollo.io/#/people?personLocations[]=United%20States&personTitles[]=ceo&qKeywords=marketing%20agency&page=1 Business Use Cases AI Automation Agencies** - Generate high-converting prospect outreach for service-based businesses B2B Sales Teams** - Create personalized cold email campaigns that actually get responses Marketing Agencies** - Offer premium personalization services to clients Consultants** - Build authority through deeply researched prospect outreach SaaS Companies** - Improve demo booking rates through personalized messaging Professional Services** - Stand out from generic sales emails with custom insights Revenue Potential This system transforms cold email economics: 5-10x Higher Response Rates** than standard cold email approaches $72K/month proven methodology** - exact system used to scale successful AI agency Premium Positioning** - prospects assume you've done extensive manual research Scalable Personalization** - process hundreds of prospects daily vs. manual research Difficulty Level: Advanced Estimated Build Time: 3-4 hours Monthly Operating Cost: ~$150 (Apollo + Apify + OpenAI + Email platform APIs) Watch My Complete Live Build Want to see me build this entire deep personalization system from scratch? I walk through every component live - including the AI prompting strategies, website scraping logic, error handling, and the exact techniques that generate 5-10% reply rates. 🎥 See My Live Build Process: "I Deep-Personalized 1000+ Cold Emails Using THIS AI System (FREE TEMPLATE)" This comprehensive tutorial shows the real development process - including advanced AI prompting, multi-page scraping architecture, and the proven icebreaker templates that have generated over $72K/month in agency revenue. Set Up Steps Apollo & Apify Integration: Configure Apify account with Apollo scraper access Set up API credentials and test lead extraction Define target audience parameters and lead qualification criteria Google Sheets Database Setup: Create multi-sheet structure (Search URLs, Leads) Configure proper column mappings for lead data Set up Google Sheets API credentials and permissions Website Scraping Infrastructure: Configure HTTP request nodes with proper redirect handling Set up error handling for websites that can't be scraped Implement intelligent batching with split-in-batches nodes AI Content Processing: Set up OpenAI API credentials with appropriate rate limits Configure dual-AI approach (page summarization + icebreaker generation) Implement token limiting to control processing costs Advanced Icebreaker Generation: Configure sophisticated AI prompting with system messages Set up example-based learning with input/output pairs Implement formatting rules for natural-sounding personalization Quality Control & Testing: Test complete workflow with small prospect batches Validate AI output quality and personalization accuracy Monitor response rates and optimize messaging templates Advanced Optimizations Scale the system with: Industry-Specific Templates:** Customize icebreaker formats for different verticals A/B Testing Framework:** Test different AI prompt variations and templates CRM Integration:** Automatically add qualified responders to sales pipelines Response Tracking:** Monitor which personalization elements drive highest engagement Multi-Touch Sequences:** Create follow-up campaigns based on initial response data Important Considerations AI Token Management:** System includes intelligent token limiting to control OpenAI costs Scraping Ethics:** Built-in delays and error handling prevent website overload Data Quality:** Filtering logic ensures only high-quality prospects with accessible websites Scalability:** Batch processing prevents IP blocking during high-volume scraping Why This System Works The key to 5-10% reply rates lies in making prospects believe you've done extensive manual research: Non-obvious details from deep website analysis Natural language patterns that avoid template detection Company name abbreviation (e.g., "Love AMS" vs "Love AMS Professional Services") Multiple page insights aggregated into compelling narratives Check Out My Channel For more advanced automation systems and proven business-building strategies that generate real revenue, explore my YouTube channel where I share the exact methodologies used to build successful automation agencies.

🤖 Create a Telegram Bot with Mistral AI and Conversation Memory A sophisticated Telegram bot that provides AI-powered responses with conversation memory. This template demonstrates how to integrate any AI API service with Telegram, making it easy to swap between different AI providers like OpenAI, Anthropic, Google AI, or any other API-based AI model. 🔧 How it works The workflow creates an intelligent Telegram bot that: 💬 Maintains conversation history for each user 🧠 Provides contextual AI responses using any AI API service 📱 Handles different message types and commands 🔄 Manages chat sessions with clear functionality 🔌 Easily adaptable to any AI provider (OpenAI, Anthropic, Google AI, etc.) ⚙️ Set up steps 📋 Prerequisites 🤖 Telegram Bot Token (from @BotFather) 🔑 AI API Key (from any AI service provider) 🚀 n8n instance with webhook capability 🛠️ Configuration Steps 🤖 Create Telegram Bot Message @BotFather on Telegram Create new bot with /newbot command Save the bot token for credentials setup 🧠 Choose Your AI Provider OpenAI: Get API key from OpenAI platform Anthropic: Sign up for Claude API access Google AI: Get Gemini API key NVIDIA: Access LLaMA models Hugging Face: Use inference API Any other AI API service 🔐 Set up Credentials in n8n Add Telegram API credentials with your bot token Add Bearer Auth/API Key credentials for your chosen AI service Test both connections 🚀 Deploy Workflow Import the workflow JSON Customize the AI API call (see customization section) Activate the workflow Set webhook URL in Telegram bot settings ✨ Features 🚀 Core Functionality 📨 Smart Message Routing**: Automatically categorizes incoming messages (commands, text, non-text) 🧠 Conversation Memory**: Maintains chat history for each user (last 10 messages) 🤖 AI-Powered Responses**: Integrates with any AI API service for intelligent replies ⚡ Command Support**: Built-in /start and /clear commands 📱 Message Types Handled 💬 Text Messages**: Processed through AI model with context 🔧 Commands**: Special handling for bot commands ❌ Non-text Messages**: Polite error message for unsupported content 💾 Memory Management 👤 User-specific chat history storage 🔄 Automatic history trimming (keeps last 10 messages) 🌐 Global state management across workflow executions 🤖 Bot Commands /start 🎯 - Welcome message with bot introduction /clear 🗑️ - Clears conversation history for fresh start Regular text 💬 - Processed by AI with conversation context 🔧 Technical Details 🏗️ Workflow Structure 📡 Telegram Trigger - Receives all incoming messages 🔀 Message Filtering - Routes messages based on type/content 💾 History Management - Maintains conversation context 🧠 AI Processing - Generates intelligent responses 📤 Response Delivery - Sends formatted replies back to user 🤖 AI API Integration (Customizable) Current Example (NVIDIA): Model: mistralai/mistral-nemotron Temperature: 0.6 (balanced creativity) Max tokens: 4096 Response limit: Under 200 words 🔄 Easy to Replace with Any AI Service: OpenAI Example: { "model": "gpt-4", "messages": [...], "temperature": 0.7, "max_tokens": 1000 } Anthropic Claude Example: { "model": "claude-3-sonnet-20240229", "messages": [...], "max_tokens": 1000 } Google Gemini Example: { "contents": [...], "generationConfig": { "temperature": 0.7, "maxOutputTokens": 1000 } } 🛡️ Error Handling ❌ Non-text message detection and appropriate responses 🔧 API failure handling ⚠️ Invalid command processing 🎨 Customization Options 🤖 AI Provider Switching To use a different AI service, modify the "NVIDIA LLaMA Chat Model" node: 📝 Change the URL in HTTP Request node 🔧 Update the request body format in "Prepare API Request" node 🔐 Update authentication method if needed 📊 Adjust response parsing in "Save AI Response to History" node 🧠 AI Behavior 📝 Modify system prompt in "Prepare API Request" node 🌡️ Adjust temperature and response parameters 📏 Change response length limits 🎯 Customize model-specific parameters 💾 Memory Settings 📊 Adjust history length (currently 10 messages) 👤 Modify user identification logic 🗄️ Customize data persistence approach 🎭 Bot Personality 🎉 Update welcome message content ⚠️ Customize error messages and responses ➕ Add new command handlers 💡 Use Cases 🎧 Customer Support**: Automated first-line support with context awareness 📚 Educational Assistant**: Homework help and learning support 👥 Personal AI Companion**: General conversation and assistance 💼 Business Assistant**: FAQ handling and information retrieval 🔬 AI API Testing**: Perfect template for testing different AI services 🚀 Prototype Development**: Quick AI chatbot prototyping 📝 Notes 🌐 Requires active n8n instance for webhook handling 💰 AI API usage may have rate limits and costs (varies by provider) 💾 Bot memory persists across workflow restarts 👥 Supports multiple concurrent users with separate histories 🔄 Template is provider-agnostic - easily switch between AI services 🛠️ Perfect starting point for any AI-powered Telegram bot project 🔧 Popular AI Services You Can Use | Provider | Model Examples | API Endpoint Style | |----------|---------------|-------------------| | 🟢 OpenAI | GPT-4, GPT-3.5 | https://api.openai.com/v1/chat/completions | | 🔵 Anthropic | Claude 3 Opus, Sonnet | https://api.anthropic.com/v1/messages | | 🔴 Google | Gemini Pro, Gemini Flash | https://generativelanguage.googleapis.com/v1beta/models/ | | 🟡 NVIDIA | LLaMA, Mistral | https://integrate.api.nvidia.com/v1/chat/completions | | 🟠 Hugging Face | Various OSS models | https://api-inference.huggingface.co/models/ | | 🟣 Cohere | Command, Generate | https://api.cohere.ai/v1/generate | Simply replace the HTTP Request node configuration to switch providers!

How it works The workflow loads a list of test cases from a Google Sheet (previous results stored from an LLM) For each test case, we execute a call to an LLM judge in parallel (using HTTP Request + Webhook nodes) The judge uses the Input, Output, and Reference Answer fields from the spreadsheet to mark each LLM response as Pass/Fail The results are logged into a separate sheet in the same Sheets file. Set up steps: Add your credentials for Google Sheets and OpenRouter (or replace the OpenRouter node with your favourite chat model). Make a copy of the example Sheet to populate it with you own test data. Run the workflow with the Execute Workflow button next to the Manual Trigger node.

Build a 100% local RAG with n8n, Ollama and Qdrant. This agent uses a semantic database (Qdrant) to answer questions about PDF files. Tutorial Click here to view the YouTube Tutorial How it works Build a chatbot that answers based on documents you provide it (Retrieval Augmented Generation). You can upload as many PDF files as you want to the Qdrant database. The chatbot will use its retrieval tool to fetch the chunks and use them to answer questions. Installation Install n8n + Ollama + Qdrant using the Self-hosted AI starter kit Make sure to install Llama 3.2 and mxbai-embed-large as embeddings model. How to use it First run the "Data Ingestion" part and upload as many PDF files as you want Run the Chatbot and start asking questions about the documents you uploaded

Set Up ElevenLabs Voice Chat Agent using Graph RAG Knowledge Graphs as Experts This workflow creates an AI voice chatbot agent that has access to several knowledge bases at the same time (used as "experts"). These knowledge bases are provided using the InfraNodus GraphRAG using the knowledge graphs and providing high-quality responses without the need to set up complex RAG vector store workflows. We use ElevenLabs to set up a voice agent that can be embedded to any website or used via their API. The advantages of using GraphRAG instead of the standard vector stores for knowledge are: Easy and quick to set up (no complex data import workflows needed) and to update with new knowledge A knowledge graph has a holistic overview of your knowledge base Better retrieval of relations between the document chunks = higher quality responses Ability to reuse in other n8n workflows How it works This template uses the n8n AI agent node as an orchestrating agent that decides which tool (knowledge graph) to use based on the user's prompt. The user's prompt is received from the ElevenLabs Conversational AI agent via an n8n Webhook, which also takes care of the voice interaction. The response from n8n is then sent to the Webhook, which is polled by the ElevenLabs voice agent. This agent processes the response and provides the final answer. Here's a description step by step: The user submits a question using ElevenLabs voice interface The question is sent via the knowledge_base tool in ElevenLabs to the n8n Webhook with the POST request containing the user's prompt and sessionID for Chat Memory node in n8n. The n8n AI agent node checks a list of tools it has access to. Each tool has a description of the knowledge auto-generated by InfraNodus (we call each tool an "expert"). The n8n AI agent decides which tool should be used to generate a response. It may reformulate user's query to be more suitable for the expert. The query is then sent to the InfraNodus HTTP node endpoint, which will query the graph that corresponds to that expert. Each InfraNodus GraphRAG expert provides a rich response that takes the whole context into account and provides a response from each expert (graph) along with a list of relevant statements retrieved using a combination or RAG and GraphRAG. The n8n AI Agent node integrates the responses received from the experts to produce the final answer. The final answer is sent back to the Webhook endpoint ElevenLabs conversational AI agent picks up the response arriving from the knowledge_base tool via the webhook and then condenses it for conversational format and transforms text into voice. How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Create a separate knowledge graph for each expert (using PDF / content import options) in InfraNodus For each graph, go to the workflow, paste the name of the graph into the body name field. Keep other settings intact or learn more about them at the InfraNodus access points page. Once you add one or more graphs as experts to your flow, add the LLM key to the OpenAI node and launch the workflow You will also need to set up an ElevenLabs account and to set up a conversational AI agent there. See the Post note in the n8n workflow for a complete step-by-step description or our support article on setting up ElevenLabs AI voice agent Once the voice AI agent is ready, you might want to combine it with a text AI chatbot workflow so your users have a choice between the text and voice interaction. In that case, you may be interested to use our free open-source website popup chat widget popupchat.dev where you can create an embed code to add to your blog or website and allow the user to choose between the text and voice interaction. Requirements An InfraNodus account and API key An OpenAI (or any other LLM) API key An ElevenLabs account FAQ 1. How many "experts" should I aim for? We recommend to aim for the number of experts as the optimal number of people in a team, which is usually 2-7. If you add more experts, your AI orchestrating agent will have troubles choosing the most suitable "expert" tool for the user's query. You can mitigate this by specifying in the AI agent description that it can choose maximum 3-7 experts to provide a response. 2. Why use InfraNodus GraphRAG and not standard vector store for knowledge? First, vector stores are complex to set up and to update. You'd need a separate workflow for that, decide on the vector dimensions, add metadata to your knowledge, etc. With InfraNodus, you have a complete RAG / GraphRAG solution under the hood that is easy to set up and provides high-quality responses that takes the overall structure and the relations between your ideas into account. 3 Why not use ElevenLabs' own knowledge? One of the reasons is that you want your knowledge base to be in one place so you can reuse it in other n8n workflows. Another reason is that you will not have such a good separation between the "experts" when you converse with the agent. So the answers you get will be based on top matches from all the books / articles you upload, while with the InfraNodus GraphRAG setup you can better control which graphs are consulted as experts and have an explicit way to display this data. Customizing this workflow You can use this same workflow with a Telegram bot, so you can interact with it using Telegram. There are many more customizations available on our GitHub repo for n8n workflows. Check out the complete setup guide for this workflow at https://support.noduslabs.com/hc/en-us/articles/20318967066396-How-to-Build-a-Text-Voice-AI-Agent-Chatbot-with-n8n-Elevenlabs-and-InfraNodus Also check out the video tutorial with a demo:

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 🎯 Overview This n8n workflow template automates the process of monitoring Instagram comments and sending predefined responses based on specific comment keywords. It integrates Instagram's Graph API with Google Sheets to manage comment responses and maintains an interaction log for customer relationship management (CRM) purposes. 🔧 Workflow Components The workflow consists of 9 main nodes organized into two primary sections: 📡 Section 1: Webhook Verification ✅ Get Verification (Webhook node) 🔄 Respond to Verification Message (Respond to Webhook node) 🤖 Section 2: Auto Comment Response 📬 Insta Update (Webhook node) ❓ Check if update is of comment? (Switch node) 👤 Comment if of other user (If node) 📊 Comment List (Google Sheets node) 💬 Send Message for Comment (HTTP Request node) 📝 Add Interaction in Sheet (CRM) (Google Sheets node) 🛠️ Prerequisites and Setup Requirements 1. 🔵 Meta/Facebook Developer Setup 📱 Create Facebook App > 📋 Action Items: > - [ ] Navigate to Facebook Developers > - [ ] Click "Create App" and select "Business" type > - [ ] Configure the following products: > - ✅ Instagram Graph API > - ✅ Facebook Login for Business > - ✅ Webhooks 🔐 Required Permissions Configure the following permissions in your Meta app: | instagram_basic | 📖 Read Instagram account profile info and media | instagram_manage_comments | 💬 Create, delete, and manage comments | instagram_manage_messages | 📤 Send and receive Instagram messages | pages_show_list | 📄 Access connected Facebook pages 🎫 Access Token Generation > ⚠️ Important Setup:+ > - [ ] Use Facebook's Graph API Explorer > - [ ] Generate a User Access Token with required permissions > - [ ] ⚡ Important: Tokens expire periodically and need refreshing 2. 🌐 Webhook Configuration 🔗 Setup Webhook URL > 📌 Configuration Checklist: > - [ ] In Meta App Dashboard, navigate to Products → Webhooks > - [ ] Subscribe to Instagram object > - [ ] Configure webhook URL: your-n8n-domain/webhook/instagram > - [ ] Set verification token (use "test" or create secure token) > - [ ] Select webhook fields: > - ✅ comments - For comment notifications > - ✅ messages - For DM notifications (if needed) ✅ Webhook Verification Process The workflow handles Meta's webhook verification automatically: 📡 Meta sends GET request with hub.challenge parameter 🔄 Workflow responds with the challenge value to confirm subscription 3. 📊 Google Sheets Setup Example - https://docs.google.com/spreadsheets/d/1ONPKJZOpQTSxbasVcCB7oBjbZcCyAm9gZ-UNPoXM21A/edit?usp=sharing 📋 Create Response Management Sheet Set up a Google Sheets document with the following structure: 📝 Sheet 1 - Comment Responses: | Column | Description | Example | |--------|-------------|---------| | 💬 Comment | Trigger keywords | "auto", "info", "help" | | 📝 Message | Corresponding response message | "Thanks for your comment! We'll get back to you soon." | 📈 Sheet 2 - Interaction Log: | Column | Description | Purpose | |--------|-------------|---------| | ⏰ Time | Timestamp of interaction | Track when interactions occur | | 🆔 User Id | Instagram user ID | Identify unique users | | 👤 Username | Instagram username | Human-readable identification | | 📝 Note | Additional notes or error messages | Debugging and analytics | 🔧 Built By - akash@codescale.tech