Field intelligence for AI-first professionalsVol. II · Nº 56 · Saturday, August 15, 2026
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760 workflows, ready to run.

Community-built n8n automations, searchable live from the template library. Open one on n8n.io and import the JSON straight into your instance.

WhatsApp RAG Chatbot with Supabase, Gemini 2.5 Flash, and OpenAI EmbeddingsWhatsApp RAG Chatbot with Supabase, Gemini 2.5 Flash, and OpenAI Embeddings This n8n template demonstrates how to build a WhatsApp-based AI chatbot that answers user questions usiHTTP Request · WhatsApp Business Cloud · AI Agent · Embeddings OpenAI · Supabase Vector Store104 views
Build a RAG-powered AI Assistant with OpenAI, Google Drive & Supabase Vector DBTarget Audience This guide is designed for developers, data scientists, and AI enthusiasts who want to create intelligent chatbots capable of understanding and using custom data. Google Drive · AI Agent · Embeddings OpenAI · OpenAI Chat Model · Recursive Character Text Splitter26 views
Company Knowledge Base Agent (RAG)Overview Turn your docs into an AI-powered internal or public-facing assistant. This chatbot workflow uses RAG (Retrieval-Augmented Generation) with Supabase vector search to answeTelegram · Google Drive · Supabase · AI Agent · Embeddings OpenAI86 views
Build a PDF-Based RAG System with OpenAI, Pinecone and Cohere RerankingThis workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow provides a complete, ready-to-use template for a Retrieval-AugmeAI Agent · Embeddings OpenAI · OpenAI Chat Model · Simple Memory · Recursive Character Text Splitter51 views
Build an MCP Server with Google Calendar and Custom FunctionsLearn how to build an MCP Server and Client in n8n with official nodes. > ⚠ Requires n8n version 1.88.0 or higher. In this example, we use Google Calendar and custom functionsHTTP Request · DebugHelper · AI Agent · OpenAI Chat Model · Simple Memory49.2k views
Build and Update RAG System with Google Drive, Qdrant, and Gemini ChatThis workflow automates the creation and management of a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as the document source. It enabHTTP Request · Google Drive · Question and Answer Chain · Embeddings OpenAI · Vector Store Retriever2k views
Discover buying-intent leads on Twitter and Instagram with GPT-4o-mini and send summaries to Slack and Notion📊 Description Automate B2B lead discovery by identifying high-intent prospects directly from Reddit discussions using AI-powered intent analysis. 🎯🤖 This workflow scans Reddit fSlack · Gmail · Notion · AI Agent · Simple Memory9 views
Build a Knowledge Base Chatbot with OpenAI, RAG and MongoDB Vector EmbeddingsWho is this for? This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of producGoogle Docs · AI Agent · Embeddings OpenAI · OpenAI Chat Model · Simple Memory8.2k views
Save Costs In RAG Workflows using the Q&A Tool With Multiple ModelsThis template shows how to use the Question and Answer tool to save costs in RAG use cases. Who is this for? This template is for everyone who wants to start giving knowledge to tAI Agent · Embeddings OpenAI · OpenAI Chat Model · Simple Vector Store · Default Data Loader3.8k views
🧑‍⚖️ AI Legal Assistant Agent — AI-Powered Legal Q&A with Document Retrieval🧑‍⚖️ AI Legal Assistant Agent — AI-Powered Legal Q&A with Document Retrieval Category: LegalTech / AI Agent / RAG / Chatbot Description: This no-code AI agent acts as a legal asTelegram · AI Agent · Embeddings OpenAI · OpenAI Chat Model · Simple Memory1.6k views
Real-time Email RAG Assistant with Gmail, OpenAI GPT, and PGVector🧠 Email real time RAG Assistant with Gmail, OpenAI & PGVector 📌 Who’s it for This workflow is ideal for: Professionals Project managers Sales and support teams Anyone maGmail · AI Agent · Embeddings OpenAI · OpenAI Chat Model · Recursive Character Text Splitter7 views
Document Q&A Chatbot with Gemini AI and Supabase Vector Search for TelegramThis template creates a Telegram AI Assistant that answers questions based on your documents, powered by Google Gemini and Supabase. Key features include Intelligent HTML Post-procTelegram · Code · AI Agent · Simple Memory · Recursive Character Text Splitter16.5k views
Local Document Question Answering with Ollama AI, Agentic RAG & PGVector🚀 n8n Local AI Agentic RAG Template Author: Jadai kongolo What is this? This template provides an entirely local implementation of an Agentic RAG (Retrieval Augmented GenerationPostgres · AI Agent · OpenAI Chat Model · Recursive Character Text Splitter · Default Data Loader541 views
🤖 AI Powered RAG Chatbot for Your Docs + Google Drive + Gemini + Qdrant🤖 AI-Powered RAG Chatbot with Google Drive Integration This workflow creates a powerful RAG (Retrieval-Augmented Generation) chatbot that can process, store, and interact with doTelegram · Google Drive · Google Docs · AI Agent · LangChain Code65.4k views
Generate Contextual Recommendations from Slack using PineconeThis advanced Retrieval-Augmented Generation (RAG) automation template for n8n enables contextual, real-time recommendations using Slack messages as input. The workflow extracts reSlack · Google Drive · AI Agent · Auto-fixing Output Parser · Structured Output Parser6 views
Generate Comprehensive Research Reports with Gemini AI and Tavily Search for Japanese UsersThis workflow contains community nodes that are only compatible with the self-hosted version of n8n. Automated Research Reports with AI and Tavily Search An intelligent research Gmail · AI Agent · Structured Output Parser · Google Gemini Chat Model6 views
RAG Chatbot for Company Documents using Google Drive and GeminiThis workflow implements a Retrieval Augmented Generation (RAG) chatbot that answers employee questions based on company documents stored in Google Drive. It automatically indexes Google Drive · AI Agent · Simple Memory · Recursive Character Text Splitter · Pinecone Vector Store94.9k views
Basic RAG chatThis workflow demonstrates a simple Retrieval-Augmented Generation (RAG) pipeline in n8n, split into two main sections: 🔹 Part 1: Load Data into Vector Store Reads files from disQuestion and Answer Chain · Embeddings Cohere · Vector Store Retriever · Recursive Character Text Splitter · Simple Vector Store2.1k views