2026年的代理框架：减少炒作，增强自主性
在过去两年中，我们已经从“带工具的大语言模型（LLMs）”发展到了真正具有自主性的系统，这些系统能够进行计划、反思、委派、重试，有时甚至以让人感到不安的方式给我们带来惊喜，仿佛它们是初级工程师。生态系统的成熟速度之快，使得框架的选择现在在很大程度上影响着你的代理能成为什么和不能成为什么。
以下是来自一位在多个技术栈中构建、破坏和重建代理的人的一手比较，重点关注实际表现而非基准测试。
首先，重大转变。在2024年，框架主要围绕提示和工具调用。在2026年，真正的区分因素是框架如何建模时间、记忆和失败。无法进行长远推理或从自身错误中学习的代理，在实际工作负载下会崩溃，无论演示中的提示工程看起来多么巧妙。
LangGraph风格的有向无环图（DAG）代理仍然受到希望控制和可预测性的团队的欢迎。其思维模型清晰，状态流动明确。调试感觉更像是在调试软件，而不是心理学。缺点是，真正开放式的行为与图形结构相悖。你可以构建自主性，但你始终意识到有轨道的存在。
以团队为导向的框架在问题能够清晰分解为角色时表现出色。研究员、规划者、执行者、审查者的角色划分在业务工作流程中仍然非常有效。当任务模糊时，这种魔力就会消失。角色边界模糊，协调开销增长超出预期。这些框架在清晰度上表现出色，而在涌现性上则不然。
AutoGPT的后代们终于明白了无界循环并不是一种特性。现代版本增加了预算、目标衰减和自我终止标准。当调优得当时，它们显得生机勃勃；当调优不佳时，它们仍会在自信地做错事的同时消耗代币。这些系统奖励那些既理解控制理论又懂得提示的团队。
2026年最有趣的类别是以记忆为核心的框架。这些系统将记忆视为一等公民，而不是简单附加的向量存储。情节记忆、语义记忆、工作记忆，所有这些都有明确的读写策略。这些代理在几天内有所提升，而不仅仅是通过对话。代价是复杂性。你不再只是构建一个代理，而是在策划一个思维。
一个安静但重要的趋势是框架边界的崩溃。最强的团队会混合使用不同的框架。图形用于安全关键路径，自主循环用于探索。人类检查点不是作为后备，而是作为设计的认知中断。那些抵制组合的框架正变得越来越过时。
对2026年剩余时间的一个预测是，获胜的框架不会宣传自主性，而是会宣传可恢复性。你能多容易地检查代理的信念、它为何采取行动以及如何在不重新开始的情况下纠正错误。未来属于那些能够犯错而不变得无用的代理。
HN社区，想知道其他人看到的是什么。不是哪个框架在理论上最好，而是哪个框架在与生产环境接触后存活下来，并让你对智能的实际运作有了不舒服的认识。
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Over the last two years we have gone from “LLMs with tools” to genuinely agentic systems that plan, reflect, delegate, retry, and sometimes surprise us in ways that feel uncomfortably close to junior engineers. The ecosystem has matured fast enough that framework choice now meaningfully shapes what your agents can and cannot become.<p>Here is a ground level comparison from someone who has built, broken, and rebuilt agents across several stacks, focusing less on benchmarks and more on lived behavior.<p>First, the big shift. In 2024, frameworks mostly wrapped prompting and tool calls. In 2026, the real differentiator is how a framework models time, memory, and failure. Agents that cannot reason over long horizons or learn from their own mistakes collapse under real workloads no matter how clever the prompt engineering looks in a demo.<p>LangGraph style DAG based agents remain popular for teams that want control and predictability. The mental model is clean. State flows are explicit. Debugging feels like debugging software rather than psychology. The downside is that truly open ended behavior fights the graph. You can build autonomy, but you are always aware of the rails.<p>Crew oriented frameworks excel when the problem decomposes cleanly into roles. Researcher, planner, executor, reviewer still works remarkably well for business workflows. The magic wears off when tasks blur. Role boundaries leak, and coordination overhead grows faster than expected. These frameworks shine in clarity, not in emergence.<p>AutoGPT descendants finally learned the lesson that unbounded loops are not a feature. Modern versions add budgeting, goal decay, and self termination criteria. When tuned well, they feel alive. When tuned poorly, they still burn tokens while confidently doing the wrong thing. These systems reward teams who understand control theory as much as prompting.<p>The most interesting category in 2026 is memory first frameworks. Systems that treat memory as a first class citizen rather than a vector store bolted on. Episodic memory, semantic memory, working memory, all with explicit read and write policies. These agents improve over days, not just conversations. The cost is complexity. You are no longer just building an agent, you are curating a mind.<p>A quiet but important trend is the collapse of framework boundaries. The strongest teams mix and match. Graphs for safety critical paths. Autonomous loops for exploration. Human checkpoints not as a fallback, but as a designed cognitive interrupt. Frameworks that resist composition feel increasingly obsolete.<p>One prediction for the rest of 2026. The winning frameworks will not advertise autonomy. They will advertise recoverability. How easily can you inspect what the agent believed, why it acted, and how to correct it without starting over. The future belongs to agents that can be wrong without being useless.<p>HN crowd, curious what others are seeing. Not which framework is best in theory, but which one survived contact with production and taught you something uncomfortable about how intelligence actually works.