<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Langgraph on Lynx Tech Blog</title><link>https://blog.lynxflow.co/en/tags/langgraph/</link><description>Recent content in Langgraph on Lynx Tech Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Fri, 31 Jul 2026 00:00:00 +0800</lastBuildDate><atom:link href="https://blog.lynxflow.co/en/tags/langgraph/index.xml" rel="self" type="application/rss+xml"/><item><title>Self-Hosted AI Agent Landscape 2026</title><link>https://blog.lynxflow.co/en/posts/self-hosted-agent-landscape-2026/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/self-hosted-agent-landscape-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/self-hosted-agent-landscape-2026.png" alt="Featured image of post Self-Hosted AI Agent Landscape 2026" /&gt;Two categories that get conflated &amp;ldquo;AI agent&amp;rdquo; in 2026 covers two very different things, and mixing them up causes most bad choices:
Build-your-own frameworks — libraries you code against to assemble an agent (LangGraph, CrewAI, AutoGPT-style). You own the plumbing: tool-calling loops, memory, channels. High control, high effort. Ready-to-run harnesses — a packaged agent you install, configure, and deploy as a service. It already has the reasoning loop, the channels, the skills system.</description></item></channel></rss>