This guide walks you through installing n8n via npx or Docker, registering an account, understanding core concepts like workflows, nodes, and connections, and building a complete AI‑driven automation that fetches Weibo hot topics, processes them with a large language model, and emails the results.
This guide walks you through building an end‑to‑end n8n workflow that reads topics from a Feishu multi‑dimensional table, generates Xiaohongshu copy and multi‑page covers with AI models, uploads the assets back to Feishu, and updates the table automatically, turning a manual content pipeline into a fully automated factory.
This article walks you through deploying a production‑grade n8n automation platform on Railway using a ready‑made template, covering Railway’s core concepts, advantages, step‑by‑step deployment, configuration, activation, and final verification, all in under five minutes.
This article examines the rise of low‑code platforms for AI agents, outlines their core benefits, and provides a detailed comparison of three popular solutions—Dify, n8n, and Coze—highlighting each platform's features, target users, strengths, and limitations to help readers choose the right tool for their projects.
This guide walks you through building an n8n workflow that automatically generates hand‑drawn doodle‑style covers for Xiaohongshu posts by configuring Volcano Engine and Zhipu AI services, creating API keys, and chaining chat, prompt, HTTP, and download nodes into a seamless design pipeline.
This guide walks through building an n8n workflow that periodically pulls hot articles from multiple RSS sources, enriches each item with a source tag, merges, filters, formats the data, writes it into a Feishu spreadsheet, and finally pushes a notification to a Feishu group, all without manual intervention.
This guide shows how to quickly and affordably deploy the n8n automation platform on Zeabur, covering core concepts, one‑click template installation, region selection, domain configuration, version upgrades, cost‑control strategies, and troubleshooting tips for a reliable workflow service.
This guide explains what n8n is, compares it with Coze and Dify, and provides step‑by‑step instructions for quick installation via npx or Docker, detailed multi‑stage Dockerfile, production environment settings, database, SSL, Redis, monitoring, and backup strategies for reliable workflow automation.
This step‑by‑step tutorial shows how to create a Supabase PostgreSQL database, copy and configure an n8n space on HuggingFace, set up the host URL, verify database connectivity, test webhook functionality, and manage the deployed project.
This article analyzes four mainstream low-code AI Agent development platforms—Coze, Dify, n8n, and LangFlow—detailing their features, deployment options, commercial models, strengths, and weaknesses, and provides a step‑by‑step learning path to help developers select the most suitable framework for 2025.
This guide introduces the n8n‑workflows open‑source collection, offering over 2,000 pre‑built JSON workflow templates categorized into 12 functional groups, and explains how to import them directly into n8n or run a local Python‑SQLite search service for instant workflow discovery.
Learn how to create a functional AI agent from scratch in just five days by leveraging GPT for the brain, n8n for automation, CrewAI for multi‑agent orchestration, Cursor for code generation, and Streamlit for a user interface, complete with step‑by‑step instructions and ready‑to‑run Python code.
This article explains how to combine Alibaba Cloud Milvus, a high‑performance vector database, with the low‑code workflow platform n8n to create an enterprise‑level, domain‑specific intelligent Q&A system, covering challenges, architecture, setup, workflow configuration, and verification steps.
This guide shows how to create a fully local Retrieval‑Augmented Generation (RAG) system using n8n, Docker, Ollama and the free Qwen3 embedding model, enabling secure, up‑to‑date AI assistants that answer enterprise questions without exposing any proprietary data.
