<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Dify on Text Matrix</title><link>https://txtmix.com/tags/dify/</link><description>Recent content in Dify on Text Matrix</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Sat, 23 May 2026 08:55:34 +0800</lastBuildDate><atom:link href="https://txtmix.com/tags/dify/index.xml" rel="self" type="application/rss+xml"/><item><title>Dify：开源 Agentic Workflow 开发平台从入门到精通指南</title><link>https://txtmix.com/posts/tech/dify-agentic-workflow-development-platform-guide/</link><pubDate>Sat, 02 May 2026 10:12:21 +0800</pubDate><guid>https://txtmix.com/posts/tech/dify-agentic-workflow-development-platform-guide/</guid><description>&lt;h2 id="前言">前言&lt;/h2>
&lt;p>大语言模型（LLM）从概念验证走向生产环境，中间隔着工程化、可靠性、可观测性三道坎。许多团队在 Prompt 调优阶段顺风顺水，却在接入真实业务流程时发现：日志往哪看？多模型怎么切换？RAG 管道怎么管理？生产流量怎么控制？这些问题的答案，往往是一套完善的 Agentic Workflow 平台才能提供的。&lt;/p></description></item></channel></rss>