Documentdefaultdataloader → In-Memory Vector Store

Inquiry-Agent. Uses @digitalocean/n8n-nodes-digitalocean-gradient-serverless-inference, stopAndError, googleDocs, gmail. Event-driven trigger; 88 nodes.

Alfred (funcional). Uses gmailTool, googleCalendarTool, gmail, embeddingsOpenAi. Event-driven trigger; 83 nodes.

Inquiry-Agent. Uses @digitalocean/n8n-nodes-digitalocean-gradient-serverless-inference, stopAndError, googleDocs, gmail. Event-driven trigger; 80 nodes.

Inquiry-Agent. Uses @digitalocean/n8n-nodes-digitalocean-gradient-serverless-inference, stopAndError, googleDocs, gmail. Event-driven trigger; 77 nodes.

Inquiry-Agent. Uses @digitalocean/n8n-nodes-digitalocean-gradient-serverless-inference, stopAndError, googleDocs, gmail. Event-driven trigger; 73 nodes.

This workflow ingests onboarding documents from Google Drive into an in-memory vector store with Google Gemini embeddings, then runs a multi-channel onboarding chatbot powered by Groq, sends role-base

Final. Uses chatTrigger, agent, n8n-nodes-upstage, httpRequest. Chat trigger; 55 nodes.

This template is a complete, hands-on tutorial for building a RAG (Retrieval-Augmented Generation) pipeline. In simple terms, you'll teach an AI to become an expert on a specific topic—in this case, t

This workflow transforms your Telegram bot into J.A.R.V.I.S., a powerful, multimodal AI assistant. It can understand and process text, voice messages, images, and documents. The assistant can search t

AI Multi-Document Analyzer with Smart Recommendations & Reporting

This n8n workflow enables an AI agent to interact with users through GoHighLevel SMS, leveraging a knowledgebase dynamically built by scraping the company's website.

This workflow implements a self-healing Retrieval-Augmented Generation (RAG) maintenance system that automatically updates document embeddings, evaluates retrieval quality, detects embedding drift, an

This workflow automates end-to-end e-commerce order processing from intake through fulfillment by orchestrating multiple AI-powered validation stages and external system integrations. Designed for e-c

This workflow automates academic research processing by routing queries through specialized AI models while maintaining contextual memory. Designed for researchers, faculty, and graduate students, it

AI Customer Support Automation. Uses gmail, openAi, httpRequest, vectorStoreInMemory. Event-driven trigger; 40 nodes.

Boost your productivity with this AI-powered email and calendar assistant:

Every day at 8 AM, the workflow automatically retrieves the latest F1 data—including driver standings, qualifying results, race schedules, and circuit information. All sources are merged into a unifie

This workflow is designed for Business Analysts, Project Managers, and Operations Teams who need to automate the creation, tracking, and delivery of Business Requirements Documents (BRDs) from submitt

Scheduled triggers initiate automated contract reviews. The system fetches documents from cloud storage and email, then uses AI to extract key terms, obligations, and compliance requirements. Multi-mo

This workflow indexes a product brochure PDF into an in-memory vector store and then responds to incoming WhatsApp text, voice, image, and video messages using OpenAI and Google Gemini, with per-custo

My workflow 2. Uses googleGemini, formTrigger, httpRequest, googleDrive. Event-driven trigger; 33 nodes.

This workflow automates academic and professional plagiarism detection by processing multi-modal submissions — documents, audio recordings, and images,through specialized AI agents. It targets educato

This workflow ingests published Notion knowledge base articles into an in-memory vector store using Cohere embeddings, then monitors a support Gmail inbox and uses Groq to draft grounded reply emails

Draft grounded support replies from a Notion knowledge base using Groq and Gmail. Uses notion, vectorStoreInMemory, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven