update README

Author: pakchoi-i

README.md

@@ -7,6 +7,7 @@
 </div>
 
 <p align="center">
+| <a href="README_ZH.md"><b>🇨🇳 中文版</b></a>
 | <a href="#-benchmark-performance"><b>🌟 Performance</b></a> 
 | <a href="#-examples"><b>💡 Examples</b> </a> 
 | <a href="#-features"><b>✨ Features</b> </a> 
@@ -20,18 +21,18 @@
 <img src="docs/assets/mascot.png" alt="Youtu-agent Logo" width="200" align="left" style="margin-right:20px;">
 
 Key highlights:
-- **Verified performance**: Achieved 71.47% on WebWalkerQA and 72.8% on GAIA (text-only subset), using purely `DeepSeek-V3` series models (without Claude or GPT).
+- **Verified performance**: Achieved 71.47% on WebWalkerQA and 72.8% on GAIA (text-only subset), using purely `DeepSeek-V3` series models (without Claude or GPT), establishing a strong open-source starting point.
 - **Open-source friendly & cost-aware**: Optimized for accessible, low-cost deployment without reliance on closed models.
-- **Practical use cases**: Out-of-the-box support for tasks like CSV analysis, literature review, personal file organization, and podcast and video generation.
-- **Flexible architecture**: Built on [openai-agents](https://github.com/openai/openai-agents-python), compatible with both `responses` and `chat.completions` APIs, supporting `DeepSeek` and `gpt-oss` models.
+- **Practical use cases**: Out-of-the-box support for tasks like CSV analysis, literature review, personal file organization, and podcast and video generation (coming soon).
+- **Flexible architecture**: Built on [openai-agents](https://github.com/openai/openai-agents-python), with extensible support for diverse model APIs (form `DeepSeek` to `gpt-oss`), tool integrations, and framework implementations.
 - **Automation & simplicity**: YAML-based configs, auto agent generation, and streamlined setup reduce manual overhead.
 
 ## 🌟 Benchmark Performance
 
 `Youtu-agent` is built on open-source models and lightweight tools, demonstrating strong results on challenging deep search and tool use benchmarks.
 
 - **[WebWalkerQA](https://huggingface.co/datasets/callanwu/WebWalkerQA)**: Achieved 60.71% accuracy with `DeepSeek-V3-0324`， using new released `DeepSeek-V3.1` can further improve to 71.47%, setting a new SOTA performance.
-- **[GAIA](https://gaia-benchmark-leaderboard.hf.space/)**: Achieved 72.8% accuracy on the text-only validation subset using `DeepSeek-V3-0324` (including models used within tools). We are actively extending evaluation to the full GAIA benchmark with multimodal tools, and conducting cross-framework experiments to ensure fair comparison, and will release the related code and data in the near future.
+- **[GAIA](https://gaia-benchmark-leaderboard.hf.space/)**: Achieved 72.8% accuracy on the [text-only validation subset](https://github.com/sunnynexus/WebThinker/blob/main/data/GAIA/dev.json) using `DeepSeek-V3-0324` (including models used within tools). We are actively extending evaluation to the full GAIA benchmark with multimodal tools, and will release the trajectories in the near future. Stay tuned! ✨
 
 ![WebWalkerQA](docs/assets/images/benchmark_webwalkerqa.png)
 
@@ -70,6 +71,23 @@ Click on the images to view detailed videos.
   </tr>
 </table>
 
+### 🤖 Automatic Agent Generation
+
+A standout feature of `Youtu-agent` is its ability to **automatically generate agent configurations**. In other frameworks, defining a task-specific agent often requires writing code or carefully crafting prompts. In contrast, `Youtu-agent` uses simple YAML-based configs, which enables streamlined automation: a built-in "meta-agent" chats with you to capture requirements, then generates and saves the config automatically.
+
+```bash
+# Interactively clarify your requirements and auto-generate a config
+python scripts/gen_simple_agent.py
+
+# Run the generated config
+python scripts/cli_chat.py --stream --config generated/xxx
+```
+
+<a href="https://www.youtube.com/watch?v=JVpHDJtKBo8">
+  <img src="https://github.com/user-attachments/assets/2a185840-732c-48df-a72f-837c836318d1" width="420" height="236">
+</a>
+
+
 For more detailed examples and advanced use-cases, please refer to the [`examples`](./examples) directory and our comprehensive documentation at [`docs/examples.md`](./docs/examples.md).
 
 
@@ -86,7 +104,7 @@ For more detailed examples and advanced use-cases, please refer to the [`example
 ### Core Features
 - **Built on openai-agents**: Leveraging the foundation of [openai-agents](https://github.com/openai/openai-agents-python) SDK, our framework inherits streaming, tracing, and agent-loop capabilities, ensuring compatibility with both `responses` and `chat.completions` APIs for seamless adaptation to diverse models like [gpt-oss](https://github.com/openai/gpt-oss).
 - **Fully asynchronous**: Enables high-performance and efficient execution, especially beneficial for evaluating benchmarks.
-- **Tracing & analysis system**: Beyond OTEL, our `DBTracingProcessor` system provides in-depth analysis of tool calls and agent trajectories.
+- **Tracing & analysis system**: Beyond OTEL, our `DBTracingProcessor` system provides in-depth analysis of tool calls and agent trajectories. (will be released soon)
 
 ### Automation
 - **YAML based configuration**: Structured and easily manageable agent configurations.
@@ -105,7 +123,7 @@ For more detailed examples and advanced use-cases, please refer to the [`example
 
 ### For Agents Researchers & LLM Trainers
 - A **simple yet powerful baseline** that is stronger than basic ReAct, serving as an excellent starting point for model training and ablation studies.
-- **Built-in support for common benchmarks** and one-click evaluation scripts to streamline the experimental process.
+- **One-click evaluation scripts** to streamline the experimental process and ensure consistent benchmarking.
 
 ### For Agent Application Developers
 - A **proven and portable scaffolding** for building real-world agent applications.
@@ -125,6 +143,8 @@ For more detailed examples and advanced use-cases, please refer to the [`example
 - **ContextManager**: A configurable module for managing the agent's context window.
 - **Benchmark**: An encapsulated workflow for a specific dataset, including preprocessing, rollout, and judging logic.
 
+For more design and implementation details, please refer to our [technical documentation](https://tencent.github.io/Youtu-agent/).
+
 ## 🚀 Getting Started
 
 First, ensure you have completed the initial setup (clone repo, install dependencies).

README_ZH.md

@@ -7,6 +7,7 @@
 </div>
 
 <p align="center">
+| <a href="README.md"><b>🇺🇸 English</b></a>
 | <a href="#-基准性能"><b>🌟 性能</b></a> 
 | <a href="#-示例"><b>💡 示例</b> </a> 
 | <a href="#-特性"><b>✨ 特性</b> </a> 
@@ -20,18 +21,18 @@
 <img src="docs/assets/mascot.png" alt="Youtu-agent Logo" width="200" align="left" style="margin-right:20px;">
 
 主要亮点：
-- **验证性能**：在 WebWalkerQA 上达到 71.47% 的准确率，在 GAIA（纯文本子集）上达到 72.8% 的准确率，纯粹使用 `DeepSeek-V3` 系列模型（不使用 Claude 或 GPT）。
+- **验证性能**：在 WebWalkerQA 上达到 71.47% 的准确率，在 GAIA（纯文本子集）上达到 72.8% 的准确率，纯粹使用 `DeepSeek-V3` 系列模型（不使用 Claude 或 GPT），建立了强大的开源起点。
 - **开源友好且成本敏感**：针对可访问、低成本部署进行了优化，不依赖封闭模型。
-- **实际用例**：开箱即用地支持 CSV 分析、文献综述、个人文件整理以及播客和视频生成等任务。
-- **灵活的架构**：基于 [openai-agents](https://github.com/openai/openai-agents-python) 构建，兼容 `responses` 和 `chat.completions` API，支持 `DeepSeek` 和 `gpt-oss` 模型。
+- **实际用例**：开箱即用地支持 CSV 分析、文献综述、个人文件整理以及播客和视频生成等任务。（即将推出）
+- **灵活的架构**：基于 [openai-agents](https://github.com/openai/openai-agents-python) 构建，可兼容各种模型 API（从 `DeepSeek` 到 `gpt-oss`）、工具集成和框架实现。
 - **自动化与简洁性**：基于 YAML 的配置、自动智能体生成和简化的设置减少了手动开销。
 
 ## 🌟 基准性能
 
 `Youtu-agent` 基于开源模型和轻量级工具构建，在具有挑战性的深度搜索和工具使用基准测试中表现出色。
 
 - **[WebWalkerQA](https://huggingface.co/datasets/callanwu/WebWalkerQA)**：使用 `DeepSeek-V3-0324` 实现了 60.71% 的准确率，使用新发布的 `DeepSeek-V3.1` 可进一步提升至 71.47%，创造了新的 SOTA 性能。
-- **[GAIA](https://gaia-benchmark-leaderboard.hf.space/)**：使用 `DeepSeek-V3-0324`（包括工具中使用的模型）在纯文本验证子集上实现了 72.8% 的准确率。我们正在积极扩展对带有多模态工具的完整 GAIA 基准的评估，并进行跨框架实验以确保公平比较，将在近期放出相关代码和数据。
+- **[GAIA](https://gaia-benchmark-leaderboard.hf.space/)**：使用 `DeepSeek-V3-0324`（包括工具中使用的模型）在[纯文本验证子集](https://github.com/sunnynexus/WebThinker?tab=readme-ov-file#benchmarks)上实现了 72.8% 的准确率。我们正在积极扩展对带有多模态工具的完整 GAIA 基准的评估，将在近期放出完整轨迹，敬请关注！✨
 
 ![WebWalkerQA](docs/assets/images/benchmark_webwalkerqa.png)
 
@@ -68,6 +69,22 @@
   </tr>
 </table>
 
+### 🤖 自动智能体生成
+
+`Youtu-agent`的突出优势在于其**自动化生成智能体及其配置**的能力。在其他框架中，定义特定任务的智能体通常需要编写代码或是精心设计提示词，而`Youtu-agent`采用基于 YAML 的简洁配置方案，实现了高效自动化：内置的“元智能体”与用户对话并捕获需求，然后自动生成并保存配置。
+
+```bash
+# Interactively clarify your requirements and auto-generate a config
+python scripts/gen_simple_agent.py
+
+# Run the generated config
+python scripts/cli_chat.py --stream --config generated/xxx
+```
+
+<a href="https://www.youtube.com/watch?v=JVpHDJtKBo8">
+  <img src="https://github.com/user-attachments/assets/2a185840-732c-48df-a72f-837c836318d1" width="420" height="236">
+</a>
+
 更详细的示例和高级用例，请参阅 [`examples`](./examples) 目录和我们的文档 [`docs/examples.md`](./docs/examples.md)。
 
 ## ✨ 特性
@@ -82,7 +99,7 @@
 ### 核心功能
 - 基于openai-agents构建：利用 [openai-agents](https://github.com/openai/openai-agents-python) SDK 作为基础，我们的框架继承了 streaming、tracing 和 agent-loop 能力，确保了与 `responses` 和 `chat.completions` API 的兼容性，无缝适应 [gpt-oss](https://github.com/openai/gpt-oss) 等多样化模型。
 - 完全异步：这实现了高性能和高效执行，尤其有利于高效的评估。
-- 追踪与分析系统：除了 OTEL，我们的 `DBTracingProcessor` 系统提供了对工具调用和智能体轨迹的深入分析。
+- 追踪与分析系统：除了 OTEL，我们的 `DBTracingProcessor` 系统提供了对工具调用和智能体轨迹的深入分析。（即将发布）
 
 ### 自动化
 - 基于 YAML 的配置：这允许结构化且易于管理的智能体配置。
@@ -100,7 +117,7 @@
 
 ### 对于智能体研究人员和大型语言模型训练师
 - 一个**简单而强大的基线**，比基本的 ReAct 更强大，可作为模型训练和消融研究的绝佳起点。
-- **内置对常见基准的支持**和一键评估脚本，以简化实验过程。
+- **一键评估脚本**用以简化实验过程，并确保一致的基准测试。
 
 ### 对于智能体应用开发者
 - 一个**经过验证且可移植的脚手架**，用于构建真实的智能体应用程序。
@@ -119,6 +136,8 @@
 - **上下文管理器（ContextManager）**：一个可配置模块，用于管理智能体的上下文窗口。
 - **基准（Benchmark）**：一个针对特定数据集的封装工作流，包括预处理、执行和判断逻辑。
 
+更多的设计与实现细节，请参阅我们的[在线文档](https://tencent.github.io/Youtu-agent/)。
+
 ## 🚀 快速开始
 
 首先，请确保您已完成初始设置（克隆仓库，安装依赖）。