ReAct是让大语言模型在完成任务时交替进行思考推理与工具调用行动的提示与执行范式，核心是让模型在思考-行动-观察的循环中不断迭代直到得到答案，能力包括推理与行动交替、循环终止控制、工具集管理与轨迹记忆。它解决的是纯文本推理拿不到实时信息与真实反馈、纯工具调用又缺少中间推理导致决策不透明的问题，并不解决工具本身质量不高的问题。
ReAct 的核心是推理与行动交替，它的作用有：
- 让模型既能推理又能获取外部信息。思考与行动交替进行，答案既来自模型推理也来自工具返回的真实数据。工具返回的观察结果会原样进入下一轮推理，模型据此修正判断而非重复同样的动作。
- 让长链任务中持续纠错。每轮循环都基于上一轮的观察重新决策，错误在过程中被修正而非累积。轨迹被完整保留后，失败案例可以定位到具体环节，改进方向从重新训练模型收敛为调整提示或工具配置。
- 让决策过程可被审计。完整的推理与行动轨迹被记录，事后能看清每一步依据了什么。
- 让工具使用有明确边界。工具集与终止条件被显式约束，防止无限循环与越权操作。
总之，ReAct 可以帮助企业实现 AI 从只会聊天到能调用工具完成实际工作的跨越，通过思考-行动-观察循环让长链任务具备纠错能力与可解释性，是从对话式助手走向能干活的企业应用的关键架构模式。
翻译
ReAct is the prompting and execution paradigm where a large language model alternates between reasoning and tool invocation while completing a task. Its core is iterating through a think-act-observe loop until reaching an answer, with capabilities covering alternating reasoning and action, loop termination control, tool set management and trajectory memory. It solves the problems of pure text reasoning being unable to obtain live information and of pure tool invocation lacking intermediate reasoning, but it does not solve poor tool quality.
At the heart of ReAct is alternating reasoning and action, which can help companies achieve the following:
- Let models both reason and obtain external information. Reasoning and action alternate, so answers draw on both model inference and real data returned by tools.
- Enable continuous correction in long chains. Each loop re-decides based on the previous observation, correcting errors in process rather than accumulating them.
- Make decisions auditable. Complete reasoning and action trajectories are recorded, so it is clear after the fact what each step relied on.
- Keep tool use explicitly bounded. Tool sets and termination conditions are constrained, preventing infinite loops and unauthorized operations.
In short, ReAct can help enterprises move AI from merely chatting to actually completing work through tools, giving long-chain tasks correction capability and explainability through the think-act-observe loop, and constitute a key architectural pattern from conversational assistants toward operational enterprise applications.
