docs: make Chinese README the default

README.md

@@ -1,3 +1,9 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/langchain-ai/langgraph) · [上游 README](https://github.com/langchain-ai/langgraph/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <div align="center">
   <a href="https://www.langchain.com/langgraph">
     <picture>
@@ -9,7 +15,7 @@
 </div>
 
 <div align="center">
-  <h3>Low-level orchestration framework for building stateful agents.</h3>
+  <h3>用于构建有状态智能体的底层编排框架。</h3>
 </div>
 
 <div align="center">
@@ -21,62 +27,62 @@
 
 <br>
 
-Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
+受到 Klarna、Replit、Elastic 等正在塑造智能体未来格局的企业的信赖，LangGraph 是一款用于构建、管理和部署长时间运行、有状态智能体的底层编排框架。
 
 ```bash
 pip install -U langgraph
 ```
 
 > [!TIP]
-> If you're looking to quickly build agents, check out **[Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview)** — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
+> 如果你想快速构建智能体，可以查看 **[Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview)** — 基于 LangGraph 构建的更高级别包，适用于能够规划、使用子智能体并利用文件系统处理复杂任务的智能体。
 
-For an equivalent JS/TS library, check out [LangGraph.js](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).
+如需等价的 JS/TS 库，请查看 [LangGraph.js](https://github.com/langchain-ai/langgraphjs) 以及 [JS 文档](https://docs.langchain.com/oss/javascript/langgraph/overview).
 
-## Why use LangGraph?
+## 为何使用 LangGraph？
 
-LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent:
+LangGraph 为*任意*长时间运行、有状态的工作流或智能体提供底层支撑基础设施：
 
-- **[Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution)** — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
-- **[Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts)** — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
-- **[Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory)** — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
-- **[Debugging with LangSmith](https://www.langchain.com/langsmith)** — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
-- **[Production-ready deployment](https://docs.langchain.com/langsmith/deployments)** — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
+- **[持久执行（Durable execution）](https://docs.langchain.com/oss/python/langgraph/durable-execution)** — 构建能够经受故障、可长时间运行，并在中断后从准确位置自动恢复的智能体。
+- **[人机协同（Human-in-the-loop）](https://docs.langchain.com/oss/python/langgraph/interrupts)** — 通过在执行过程中随时检查和修改智能体状态，无缝融入人工监督。
+- **[全面记忆（Comprehensive memory）](https://docs.langchain.com/oss/python/langgraph/memory)** — 创建真正有状态的智能体，同时具备用于持续推理的短期工作记忆，以及跨会话的长期持久记忆。
+- **[使用 LangSmith 调试](https://www.langchain.com/langsmith)** — 借助可视化工具深入了解复杂智能体行为，追踪执行路径、捕获状态转换，并提供详细的运行时指标。
+- **[生产级部署（Production-ready deployment）](https://docs.langchain.com/langsmith/deployments)** — 借助专为有状态、长时间运行工作流独特挑战而设计的可扩展基础设施，自信地部署复杂的智能体系统。
 
 > [!TIP]
-> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).
+> 如需开发、调试和部署 AI 智能体与 LLM 应用，请参阅 [LangSmith](https://docs.langchain.com/langsmith/home).
 
-## LangGraph ecosystem
+## LangGraph 生态系统
 
-While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
+LangGraph 可独立使用，也能与任何 LangChain 产品无缝集成，为开发者提供构建智能体的全套工具。
 
-To improve your LLM application development, pair LangGraph with:
+为提升 LLM 应用开发效率，可将 LangGraph 与以下产品搭配使用：
 
-- [Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
-- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
-- [LangSmith](https://www.langchain.com/langsmith) – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in [LangSmith Studio](https://docs.langchain.com/langsmith/studio).
+- [Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) – 构建能够规划、使用子智能体并利用文件系统处理复杂任务的智能体。
+- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – 提供集成与可组合组件，简化 LLM 应用开发。
+- [LangSmith](https://www.langchain.com/langsmith) – 有助于智能体评估与可观测性。调试表现不佳的 LLM 应用运行、评估智能体轨迹、获得生产环境可见性，并随时间提升性能。
+- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) – 借助专为长时间运行、有状态工作流打造的部署平台，轻松部署和扩展智能体。在团队间发现、复用、配置和共享智能体——并通过 [LangSmith Studio](https://docs.langchain.com/langsmith/studio). 中的可视化原型设计快速迭代。
 
 ---
 
-## Documentation
+## 文档
 
-- [docs.langchain.com](https://docs.langchain.com/oss/python/langgraph/overview) – Comprehensive documentation, including conceptual overviews and guides
-- [reference.langchain.com/python/langgraph](https://reference.langchain.com/python/langgraph) – API reference docs for LangGraph packages
-- [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart) – Get started building with LangGraph
-- [Chat LangChain](https://chat.langchain.com/) – Chat with the LangChain documentation and get answers to your questions
+- [docs.langchain.com](https://docs.langchain.com/oss/python/langgraph/overview) – 全面文档，包括概念概览与指南
+- [reference.langchain.com/python/langgraph](https://reference.langchain.com/python/langgraph) – LangGraph 软件包的 API 参考文档
+- [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart) – 开始使用 LangGraph 进行构建
+- [Chat LangChain](https://chat.langchain.com/) – 与 LangChain 文档对话，获取问题解答
 
-**Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.
+**讨论**：访问 [LangChain Forum](https://forum.langchain.com) 与社区交流，分享你的技术问题、想法与反馈。
 
-## Additional resources
+## 更多资源
 
-- **[Guides](https://docs.langchain.com/oss/python/learn)** – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
-- **[LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph)** – Learn the basics of LangGraph in our free, structured course.
-- **[Case studies](https://www.langchain.com/built-with-langgraph)** – Hear how industry leaders use LangGraph to ship AI applications at scale.
-- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) – Learn how to contribute to LangChain projects and find good first issues.
-- [Code of Conduct](https://github.com/langchain-ai/langchain/?tab=coc-ov-file) – Our community guidelines and standards for participation.
+- **[指南（Guides）](https://docs.langchain.com/oss/python/learn)** – 针对流式传输、添加记忆与持久化、设计模式（如分支、子图等）等主题的快速、可操作的代码片段。
+- **[LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph)** – 在我们的免费结构化课程中学习 LangGraph 基础知识。
+- **[案例研究（Case studies）](https://www.langchain.com/built-with-langgraph)** – 了解行业领导者如何借助 LangGraph 大规模交付 AI 应用。
+- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) – 了解如何为 LangChain 项目做贡献，并找到适合新手的议题。
+- [Code of Conduct](https://github.com/langchain-ai/langchain/?tab=coc-ov-file) – 我们的社区准则与参与标准。
 
 ---
 
-## Acknowledgements
+## 致谢
 
-LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
+LangGraph 的灵感来自 [Pregel](https://research.google/pubs/pub37252/) 和 [Apache Beam](https://beam.apache.org/). 公共接口的设计借鉴了 [NetworkX](https://networkx.org/documentation/latest/). LangGraph 由 LangChain 的创建者 LangChain Inc 构建，但无需 LangChain 亦可使用。