SemiAnalysis解释说，代理AI任务中的“框架”是上下文编排工具，而非魔法。这些框架将请求路由到LLM服务器，格式化输入，并管理对话历史，因为模型本身是无状态的。Claude Code、Codex和OpenCode等各种框架之间的关键区别在于它们的上下文管理和用户界面功能，而不是Opus或GPT 5.5等核心模型。 AI
影响 阐明了AI“框架”作为上下文管理器所扮演的角色，强调了模型能力（而不仅仅是编排）驱动着代理任务的性能。
排序理由 该集群包含一个多部分X线程，解释了AI框架的技术概念，而不是主要发布或重大事件。
AI 生成摘要 · Google Gemini · 来自 5 个来源。 我们如何撰写摘要 →
So while all harnesses make slightly different decisions while performing this "plan, act, verify" pattern, this loop is the common skeleton driving all agentic tasks. So let's stop talking about harnesses like they're magic! While good harness engineering helps, the real power h…
When you send a message, the harness will route your request to the appropriate LLM server, then apply some chat templates to convert the HTTP request to something the model can better understand. The harness will also add more advanced parameters to control cache, max output htt…
So a "harness" is really a context orchestration tool. Every request body it builds typically has the same three parts: 🟠 System Prompt: the "you are Claude Code" setup, plus injected stuff like your file tree or recent commits 🟠 Tool Definitions: a list of JSON schemas
It is first helpful to understand how the underlying models work. Opus, GPT 5.5, etc (the models) are all stateless -- they remember nothing between requests. That is, each time you press "enter" at the prompt factory, the harness rebuilds the entire conversation and ships it htt…
Everyone's always talking about agentic coding harnesses: Claude Code, Codex, OpenCode, Pi... the list goes on. But what's the difference between all of them? What even Is a harness anyway? In this thread, we'll take a look under the hood. (1/5)🧵 https://t.co/jc4lTbeccI