这篇论文对比了三种不同的LLM智能体工具，揭示了它们在架构上的相似之处和差异，对于想要了解LLM智能体架构发展的人来说非常有价值。
本文对比研究了三种基于不同哲学的开放编码智能体工具：LangChain的deepagents、Earendil的pi和DeepSeek的dsh。研究发现，两种成熟的工具在架构上走向了相反的方向，但都趋向于一种包含五个常见元素的中间形式：商品化循环、只读可重放会话记录、将模型特性作为数据保留、逐步披露上下文和显式扩展接口。第三种工具在后续阅读中表现出所有五个元素，并在一个接口中直接重用了另一个工具的实现。此外，外部可验证性这一关键维度并未出现收敛，这被视为预测性差距，是未来工具在来源敏感领域差异化的下一个轴心。
The Empire, Long Divided, Must Unite: Architectural Convergence in Three LLM Agent Harnesses
An agent harness is what turns a language model into an autonomous agent: the surrounding code that builds the model's context, mediates its tools, runs the loop, and persists state across a long-horizon run. This layer, not the model it wraps, is increasingly the binding constraint on agent behaviour. We present a source-level, multi-case study of three open coding-agent harnesses built from deliberately opposing philosophies: LangChain's deepagents (batteries-included), Earendil's pi (radical minimalism), and DeepSeek's dsh (everything-is-a-plugin). Reading each at a pinned commit and following its commit history, we find that the two mature harnesses have travelled in opposite directions (deepagents subtracting authored scaffolding, pi accreting durable infrastructure), yet converged toward one architectural middle form of five recurring elements: a commoditised loop, an append-only replayable session record, model quirks kept as data, progressive disclosure of context, and explicit extension seams. A third harness, read afterward as a held-out check, exhibits all five, and in one seam reuses another's implementation outright. We therefore do not claim independent invention, and decompose the convergence into parallel discovery, diffusion, and literal reuse. Finally, one load-bearing dimension shows no convergence, and indeed no presence: external verifiability, a tamper-evident record an outside party can check without trusting the runtime. We read this absence not as an oversight but as a predictive gap, the next axis on which harnesses for provenance-sensitive domains will differ.