diff --git a/README.md b/README.md
index 2be94bf..b554acf 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,9 @@
+
+> [!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/microsoft/agent-lightning) · [上游 README](https://github.com/microsoft/agent-lightning/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。
+
@@ -11,81 +17,81 @@
[](https://deepwiki.com/microsoft/agent-lightning)
[](https://discord.gg/RYk7CdvDR7)
-**The absolute trainer to light up AI agents.**
+**点亮 AI 智能体的终极训练器。**
-Join our [Discord community](https://discord.gg/RYk7CdvDR7) to connect with other users and contributors.
+加入我们的 [Discord 社区](https://discord.gg/RYk7CdvDR7)，与其他用户和贡献者交流。
-## ⚡ Core Features
+## ⚡ 核心特性
-- Turn your agent into an optimizable beast with **ZERO CODE CHANGE** (almost)! 💤
-- Build with **ANY** agent framework (LangChain, OpenAI Agent SDK, AutoGen, CrewAI, Microsoft Agent Framework...); or even WITHOUT agent framework (Python OpenAI). You name it! 🤖
-- **Selectively** optimize one or more agents in a multi-agent system. 🎯
-- Embraces **Algorithms** like Reinforcement Learning, Automatic Prompt Optimization, Supervised Fine-tuning and more. 🤗
+- 让你的智能体在几乎 **零代码改动（ZERO CODE CHANGE）** 的情况下变成可优化的强者！💤
+- 支持使用 **任意（ANY）** 智能体框架（LangChain、OpenAI Agent SDK、AutoGen、CrewAI、Microsoft Agent Framework...）构建；甚至无需智能体框架（Python OpenAI）也能用。随心所欲！🤖
+- 在多智能体系统中 **选择性（Selectively）** 优化一个或多个智能体。🎯
+- 支持 **强化学习（Reinforcement Learning）**、自动提示词优化（Automatic Prompt Optimization）、监督微调（Supervised Fine-tuning）等多种 **算法（Algorithms）**。🤗
-Read more on our [documentation website](https://microsoft.github.io/agent-lightning/).
+更多内容请参阅我们的[文档网站](https://microsoft.github.io/agent-lightning/).
-## ⚡ Installation
+## ⚡ 安装
```bash
pip install agentlightning
```
-For the latest nightly build (cutting-edge features), you can install from Test PyPI:
+如需最新每夜构建版本（前沿功能），可从 Test PyPI 安装：
```bash
pip install --upgrade --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ --pre agentlightning
```
-Please refer to our [installation guide](https://microsoft.github.io/agent-lightning/stable/tutorials/installation/) for more details.
+更多细节请参阅我们的[安装指南](https://microsoft.github.io/agent-lightning/stable/tutorials/installation/)
-To start using Agent-lightning, check out our [documentation](https://microsoft.github.io/agent-lightning/) and [examples](./examples).
+要开始使用 Agent-lightning，请查看我们的[文档](https://microsoft.github.io/agent-lightning/)和[示例](./examples)。
-## ⚡ Articles
+## ⚡ 文章
-- 12/17/2025 [Adopting the Trajectory Level Aggregation for Faster Training](https://agent-lightning.github.io/posts/trajectory_level_aggregation/) Agent-lightning blog.
-- 11/4/2025 [Tuning ANY AI agent with Tinker ✕ Agent-lightning](https://medium.com/@yugez/tuning-any-ai-agent-with-tinker-agent-lightning-part-1-1d8c9a397f0e) Medium. See also [Part 2](https://medium.com/@yugez/tuning-any-ai-agent-with-tinker-agent-lightning-part-2-332c5437f0dc).
-- 10/22/2025 [No More Retokenization Drift: Returning Token IDs via the OpenAI Compatible API Matters in Agent RL](https://blog.vllm.ai/2025/10/22/agent-lightning.html) vLLM blog. See also [Zhihu writeup](https://zhuanlan.zhihu.com/p/1965067274642785725).
-- 8/11/2025 [Training AI Agents to Write and Self-correct SQL with Reinforcement Learning](https://medium.com/@yugez/training-ai-agents-to-write-and-self-correct-sql-with-reinforcement-learning-571ed31281ad) Medium.
-- 8/5/2025 [Agent Lightning: Train ANY AI Agents with Reinforcement Learning](https://arxiv.org/abs/2508.03680) arXiv paper.
-- 7/26/2025 [We discovered an approach to train any AI agent with RL, with (almost) zero code changes.](https://www.reddit.com/r/LocalLLaMA/comments/1m9m670/we_discovered_an_approach_to_train_any_ai_agent/) Reddit.
-- 6/6/2025 [Agent Lightning - Microsoft Research](https://www.microsoft.com/en-us/research/project/agent-lightning/) Project page.
+- 12/17/2025 [采用轨迹级聚合实现更快训练](https://agent-lightning.github.io/posts/trajectory_level_aggregation/) Agent-lightning 博客。
+- 11/4/2025 [使用 Tinker ✕ Agent-lightning 调优任意 AI 智能体](https://medium.com/@yugez/tuning-any-ai-agent-with-tinker-agent-lightning-part-1-1d8c9a397f0e) Medium。另见 [Part 2](https://medium.com/@yugez/tuning-any-ai-agent-with-tinker-agent-lightning-part-2-332c5437f0dc).
+- 10/22/2025 [告别 Retokenization 漂移：在智能体 RL 中通过 OpenAI 兼容 API 返回 Token ID 的重要性](https://blog.vllm.ai/2025/10/22/agent-lightning.html) vLLM 博客。另见 [知乎文章](https://zhuanlan.zhihu.com/p/1965067274642785725).
+- 8/11/2025 [用强化学习训练 AI 智能体编写并自我纠正 SQL](https://medium.com/@yugez/training-ai-agents-to-write-and-self-correct-sql-with-reinforcement-learning-571ed31281ad) Medium。
+- 8/5/2025 [Agent Lightning：用强化学习训练任意 AI 智能体](https://arxiv.org/abs/2508.03680) arXiv 论文。
+- 7/26/2025 [我们发现了一种用 RL 训练任意 AI 智能体的方法，几乎无需修改代码。](https://www.reddit.com/r/LocalLLaMA/comments/1m9m670/we_discovered_an_approach_to_train_any_ai_agent/) Reddit。
+- 6/6/2025 [Agent Lightning - Microsoft Research](https://www.microsoft.com/en-us/research/project/agent-lightning/) 项目页面。
-## ⚡ Community Projects
+## ⚡ 社区项目
-- [DeepWerewolf](https://github.com/af-74413592/DeepWerewolf) — A case study of agent RL training for the Chinese Werewolf game built with AgentScope and Agent Lightning.
-- [AgentFlow](https://agentflow.stanford.edu/) — A modular multi-agent framework that combines planner, executor, verifier, and generator agents with the Flow-GRPO algorithm to tackle long-horizon, sparse-reward tasks.
-- [Youtu-Agent](https://github.com/TencentCloudADP/Youtu-agent) — Youtu-Agent lets you build and train your agent with ease. Built with [a modified branch](https://github.com/microsoft/agent-lightning/tree/contrib/youtu-agent-lightning) of Agent Lightning, Youtu-Agent has verified up to 128 GPUs RL training on maths/code and search capabilities with steady convergence. Also check [the recipe](https://github.com/TencentCloudADP/youtu-agent/tree/rl/agl) and their blog [*Stop Wrestling with Your Agent RL: How Youtu-Agent Achieved Stable, 128-GPU Scaling Without Breaking a Sweat*](https://spotted-coconut-df8.notion.site/Stop-Wrestling-with-Your-Agent-RL-How-Youtu-Agent-Achieved-Stable-128-GPU-Scaling-Without-Breaking-2ca5e8f089ba80539a98c582b65e0233).
+- [DeepWerewolf](https://github.com/af-74413592/DeepWerewolf) — 基于 AgentScope 与 Agent Lightning 构建的中文狼人杀智能体强化学习训练案例研究。
+- [AgentFlow](https://agentflow.stanford.edu/) — 模块化多智能体框架，结合规划器、执行器、验证器与生成器智能体及 Flow-GRPO 算法，用于解决长视野、稀疏奖励任务。
+- [Youtu-Agent](https://github.com/TencentCloudADP/Youtu-agent) — Youtu-Agent 让你轻松构建并训练智能体。基于 Agent Lightning 的[修改分支](https://github.com/microsoft/agent-lightning/tree/contrib/youtu-agent-lightning)构建，Youtu-Agent 已在数学/代码与搜索能力上验证了多达 128 GPU 的稳定收敛强化学习训练。还可查看[配方](https://github.com/TencentCloudADP/youtu-agent/tree/rl/agl)及其博客[*别再与智能体 RL 较劲：Youtu-Agent 如何轻松实现稳定的 128 GPU 扩展*](https://spotted-coconut-df8.notion.site/Stop-Wrestling-with-Your-Agent-RL-How-Youtu-Agent-Achieved-Stable-128-GPU-Scaling-Without-Breaking-2ca5e8f089ba80539a98c582b65e0233).
-## ⚡ Architecture
+## ⚡ 架构
-Agent Lightning keeps the moving parts to a minimum so you can focus on your idea, not the plumbing. Your agent continues to run as usual; you can still use any agent framework you like; you drop in the lightweight `agl.emit_xxx()` helper, or let the tracer collect every prompt, tool call, and reward. Those events become structured spans that flow into the LightningStore, a central hub that keeps tasks, resources, and traces in sync.
+Agent Lightning 将运行组件精简到最少，让你专注于创意本身，而非底层 plumbing。你的智能体照常运行；你仍可选用任意智能体框架；只需接入轻量级的 `agl.emit_xxx()` 辅助工具，或让 tracer 采集每一次 prompt、工具调用与 reward。这些事件会成为结构化 span，流入 LightningStore——一个集中式枢纽，负责同步任务、资源与 trace。
-On the other side of the store sits the algorithm you choose, or write yourself. The algorithm reads spans, learns from them, and posts updated resources such as refined prompt templates or new policy weights. The Trainer ties it all together: it streams datasets to runners, ferries resources between the store and the algorithm, and updates the inference engine when improvements land. You can either stop there, or simply let the same loop keep turning.
+Store 的另一端是你选择或自行编写的算法。算法读取 span、从中学习，并发布更新后的资源，例如优化后的 prompt 模板或新的 policy 权重。Trainer 将所有环节串联起来：向 runner 流式传输数据集，在 store 与算法之间传递资源，并在改进生效时更新推理引擎。你可以在此止步，也可以让同一循环持续运转。
-No rewrites, no lock-in, just a clear path from first rollout to steady improvement.
+无需重写、无需绑定，从首次 rollout 到持续改进，路径清晰明了。
-## ⚡ CI Status
+## ⚡ CI 状态
-| Workflow | Status |
+| 工作流 | 状态 |
|----------|--------|
-| CPU Tests | [](https://github.com/microsoft/agent-lightning/actions/workflows/tests.yml) |
-| Full Tests | [](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml) |
-| UI Tests | [](https://github.com/microsoft/agent-lightning/actions/workflows/dashboard.yml) |
-| Examples Integration | [](https://github.com/microsoft/agent-lightning/actions/workflows/badge-examples.yml) |
-| Latest Dependency Compatibility | [](https://github.com/microsoft/agent-lightning/actions/workflows/badge-latest.yml) |
-| Legacy Examples Compatibility | [](https://github.com/microsoft/agent-lightning/actions/workflows/badge-compat.yml) |
+| CPU 测试 | [](https://github.com/microsoft/agent-lightning/actions/workflows/tests.yml) |
+| 完整测试 | [](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml) |
+| UI 测试 | [](https://github.com/microsoft/agent-lightning/actions/workflows/dashboard.yml) |
+| 示例集成 | [](https://github.com/microsoft/agent-lightning/actions/workflows/badge-examples.yml) |
+| 最新依赖兼容性 | [](https://github.com/microsoft/agent-lightning/actions/workflows/badge-latest.yml) |
+| 旧版示例兼容性 | [](https://github.com/microsoft/agent-lightning/actions/workflows/badge-compat.yml) |
-## ⚡ Citation
+## ⚡ 引用
-If you find Agent Lightning useful in your research or projects, please cite our paper:
+如果你在研究或项目中觉得 Agent Lightning 有帮助，请引用我们的论文：
```bibtex
@misc{luo2025agentlightningtrainai,
@@ -99,22 +105,22 @@ If you find Agent Lightning useful in your research or projects, please cite our
}
```
-## ⚡ Contributing
+## ⚡ 贡献
-This project welcomes contributions and suggestions. Start by reading the [Contributing Guide](docs/community/contributing.md) for recommended contribution points, environment setup, branching conventions, and pull request expectations. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
+本项目欢迎贡献与建议。请先阅读[贡献指南](docs/community/contributing.md)，了解推荐的贡献切入点、环境配置、分支约定与 Pull Request 要求。多数贡献需签署贡献者许可协议（Contributor License Agreement，CLA），声明你有权且确实授予我们使用你贡献的权利。详情请访问 https://cla.opensource.microsoft.com.
-When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
+当你提交拉取请求（pull request）时，CLA 机器人会自动判断你是否需要提供 CLA（Contributor License Agreement，贡献者许可协议），并会以合适的方式标注该 PR（例如状态检查、评论）。只需按照机器人提供的说明操作即可。在使用我们 CLA 的所有仓库中，你只需完成一次此流程。
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
+本项目已采用 [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 了解更多信息，请参阅 [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) 或通过 [opencode@microsoft.com](mailto:opencode@microsoft.com) 联系我们，提出任何其他问题或意见。
-## ⚡ Trademarks
+## ⚡ 商标
-This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
+本项目可能包含项目、产品或服务的商标或标识。Microsoft 商标或标识的授权使用须遵守并遵循 [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). 在本项目的修改版本中使用 Microsoft 商标或标识，不得造成混淆或暗示 Microsoft 的赞助。任何第三方商标或标识的使用均须遵守该第三方的相关政策。
-## ⚡ Responsible AI
+## ⚡ 负责任 AI（Responsible AI）
-This project has been evaluated and certified to comply with the Microsoft Responsible AI Standard. The team will continue to monitor and maintain the repository, addressing any severe issues, including potential harms, if they arise.
+本项目已经过评估和认证，符合 Microsoft Responsible AI Standard（微软负责任 AI 标准）。团队将持续监控和维护该仓库，并在出现任何严重问题（包括潜在危害）时予以处理。
-## ⚡ License
+## ⚡ 许可证
-This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+本项目采用 MIT License 许可。详情请参阅 [LICENSE](LICENSE) 文件。