请问HN：什么使得一个AI代理框架具备生产就绪的能力，而不仅仅是一个玩具？
我一直在评估各种AI代理框架（如LangChain、CrewAI、AutoGPT、OpenClaw等），并试图弄清楚哪些因素使得某些框架在生产环境中有效，而其他框架仅仅是有趣的演示。
我目前的“生产就绪”检查清单如下：
1. 跨会话的持久记忆（不仅仅是上下文窗口的填充）
2. 真实工具的使用与错误恢复（文件输入/输出、命令行、浏览器、API）
3. 多模型支持（在Claude、GPT、本地模型之间切换而无需重写）
4. 通过技能/插件系统实现可扩展性，而不是硬编码的链条
5. 作为守护进程/服务运行，而不仅仅是手动调用的命令行工具
6. 安全边界——沙箱、权限模型、审计日志
我注意到大多数框架在这些方面中的1-2项做得很好，但在其他方面却表现不佳。那些为演示而构建的框架往往有华丽的用户界面，但在你尝试无人值守运行一周时就会崩溃。
你的检查清单是什么？你见过哪些模式将真正的代理基础设施与周末项目区分开来？
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I've been evaluating AI agent frameworks (LangChain, CrewAI, AutoGPT, OpenClaw, etc.) and I'm trying to figure out what separates the ones that actually work in production from the ones that are fun demos.<p>My current checklist for "production-ready":<p>1. Persistent memory across sessions (not just in-context window stuffing)
2. Real tool use with error recovery (file I/O, shell, browser, APIs)
3. Multi-model support (swap between Claude, GPT, local models without rewriting)
4. Extensibility via a skill/plugin system rather than hardcoded chains
5. Runs as a daemon/service, not just a CLI you invoke manually
6. Security boundaries — sandboxing, permission models, audit logs<p>What I've noticed is most frameworks nail 1-2 of these but fall apart on the rest. The ones built for demos tend to have flashy UIs but break when you try to run them unattended for a week.<p>What's your checklist? What patterns have you seen that separate real agent infrastructure from weekend projects?