docs: make Chinese README the default

2026-07-13 10:13:27 +00:00
parent e7e04b78b4
commit 5d94e8e5dc
1 changed files with 116 additions and 110 deletions
@@ -1,26 +1,32 @@
+<!-- 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/simular-ai/Agent-S) · [上游 README](https://github.com/simular-ai/Agent-S/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。
+
 <h1 align="center">
  <img src="images/agent_s.png" alt="Logo" style="vertical-align:middle" width="60"> Agent S:
-  <small>Use Computer Like a Human</small>
+  <small>像人类一样使用计算机</small>
 </h1>

-<h2 align="center">🏆 Agent S3: First to Surpass Human Performance on OSWorld (72.60%)</h2>
+<h2 align="center">🏆 Agent S3：首个在 OSWorld 上超越人类表现（72.60%）</h2>

 <p align="center">&nbsp;
-  🌐 <a href="https://www.simular.ai/articles/agent-s3">[S3 blog]</a>&nbsp;
-  📄 <a href="https://arxiv.org/abs/2510.02250">[S3 Paper]</a>&nbsp;
-  🎥 <a href="https://www.youtube.com/watch?v=VHr0a3UBsh4">[S3 Video]</a>
+  🌐 <a href="https://www.simular.ai/articles/agent-s3">[S3 博客]</a>&nbsp;
+  📄 <a href="https://arxiv.org/abs/2510.02250">[S3 论文]</a>&nbsp;
+  🎥 <a href="https://www.youtube.com/watch?v=VHr0a3UBsh4">[S3 视频]</a>
 </p>

 <p align="center">&nbsp;
-  🌐 <a href="https://www.simular.ai/articles/agent-s2-technical-review">[S2 blog]</a>&nbsp;
-  📄 <a href="https://arxiv.org/abs/2504.00906">[S2 Paper (COLM 2025)]</a>&nbsp;
-  🎥 <a href="https://www.youtube.com/watch?v=wUGVQl7c0eg">[S2 Video]</a>
+  🌐 <a href="https://www.simular.ai/articles/agent-s2-technical-review">[S2 博客]</a>&nbsp;
+  📄 <a href="https://arxiv.org/abs/2504.00906">[S2 论文（COLM 2025）]</a>&nbsp;
+  🎥 <a href="https://www.youtube.com/watch?v=wUGVQl7c0eg">[S2 视频]</a>
 </p>

 <p align="center">&nbsp;
-  🌐 <a href="https://www.simular.ai/agent-s">[S1 blog]</a>&nbsp;
-  📄 <a href="https://arxiv.org/abs/2410.08164">[S1 Paper (ICLR 2025)]</a>&nbsp;
-  🎥 <a href="https://www.youtube.com/watch?v=OBDE3Knte0g">[S1 Video]</a>
+  🌐 <a href="https://www.simular.ai/agent-s">[S1 博客]</a>&nbsp;
+  📄 <a href="https://arxiv.org/abs/2410.08164">[S1 论文（ICLR 2025）]</a>&nbsp;
+  🎥 <a href="https://www.youtube.com/watch?v=OBDE3Knte0g">[S1 视频]</a>
 </p>

 <p align="center">&nbsp;
@@ -54,101 +60,101 @@

 <div align="center">
  &nbsp;&nbsp;
-<p>Skip the setup? Try Agent S in <a href="https://cloud.simular.ai/">Simular Cloud</a>
+<p>跳过配置？在 <a href="https://cloud.simular.ai/">Simular Cloud</a> 中试用 Agent S
 </div>

-## 🥳 Updates
- [x] **2025/12/15**: Agent S3 is the **first** to surpass human-level performance on OSWorld with an impressive score of **72.60%**!
- [x] **2025/10/02**: Released Agent S3 and its [technical paper](https://arxiv.org/abs/2510.02250), setting a new SOTA of **69.9%** on OSWorld (approaching 72% human performance), with strong generalizability on WindowsAgentArena and AndroidWorld! It is also simpler, faster, and more flexible.
- [x] **2025/08/01**: Agent S2.5 is released (gui-agents v0.2.5): simpler, better, and faster! New SOTA on [OSWorld-Verified](https://os-world.github.io)!
- [x] **2025/07/07**: The [Agent S2 paper](https://arxiv.org/abs/2504.00906) is accepted to COLM 2025! See you in Montreal!
- [x] **2025/04/27**: The Agent S paper won the Best Paper Award 🏆 at ICLR 2025 Agentic AI for Science Workshop!
- [x] **2025/04/01**: Released the [Agent S2 paper](https://arxiv.org/abs/2504.00906) with new SOTA results on OSWorld, WindowsAgentArena, and AndroidWorld!
- [x] **2025/03/12**: Released Agent S2 along with v0.2.0 of [gui-agents](https://github.com/simular-ai/Agent-S), the new state-of-the-art for computer use agents (CUA), outperforming OpenAI's CUA/Operator and Anthropic's Claude 3.7 Sonnet Computer-Use!
- [x] **2025/01/22**: The [Agent S paper](https://arxiv.org/abs/2410.08164) is accepted to ICLR 2025!
- [x] **2025/01/21**: Released v0.1.2 of [gui-agents](https://github.com/simular-ai/Agent-S) library, with support for Linux and Windows!
- [x] **2024/12/05**: Released v0.1.0 of [gui-agents](https://github.com/simular-ai/Agent-S) library, allowing you to use Agent-S for Mac, OSWorld, and WindowsAgentArena with ease!
- [x] **2024/10/10**: Released the [Agent S paper](https://arxiv.org/abs/2410.08164) and codebase!
+## 🥳 更新
+- [x] **2025/12/15**：Agent S3 **首次**在 OSWorld 上以 **72.60%** 的出色成绩超越人类水平表现！
+- [x] **2025/10/02**：发布 Agent S3 及其[技术论文](https://arxiv.org/abs/2510.02250), setting a new SOTA of **69.9%** on OSWorld (approaching 72% human performance), with strong generalizability on WindowsAgentArena and AndroidWorld! It is also simpler, faster, and more flexible.
+- [x] **2025/08/01**：发布 Agent S2.5（gui-agents v0.2.5）：更简单、更好、更快！[OSWorld-Verified](https://os-world.github.io)! 上创下新的 SOTA
+- [x] **2025/07/07**：[Agent S2 论文](https://arxiv.org/abs/2504.00906) 被 COLM 2025 接收！蒙特利尔见！
+- [x] **2025/04/27**：Agent S 论文在 ICLR 2025 Agentic AI for Science Workshop 荣获最佳论文奖 🏆！
+- [x] **2025/04/01**：发布 [Agent S2 论文](https://arxiv.org/abs/2504.00906)，在 OSWorld、WindowsAgentArena 和 AndroidWorld 上取得新的 SOTA 结果！
+- [x] **2025/03/12**：发布 Agent S2 以及 [gui-agents](https://github.com/simular-ai/Agent-S), v0.2.0，成为计算机使用智能体（CUA）的新标杆，性能超越 OpenAI 的 CUA/Operator 和 Anthropic 的 Claude 3.7 Sonnet Computer-Use！
+- [x] **2025/01/22**：[Agent S 论文](https://arxiv.org/abs/2410.08164) 被 ICLR 2025 接收！
+- [x] **2025/01/21**：发布 [gui-agents](https://github.com/simular-ai/Agent-S) v0.1.2 库，支持 Linux 和 Windows！
+- [x] **2024/12/05**：发布 [gui-agents](https://github.com/simular-ai/Agent-S) v0.1.0 库，让你轻松在 Mac、OSWorld 和 WindowsAgentArena 上使用 Agent-S！
+- [x] **2024/10/10**：发布 [Agent S 论文](https://arxiv.org/abs/2410.08164) 及代码库！

-## Table of Contents
+## 目录

-1. [💡 Introduction](#-introduction)
-2. [🎯 Current Results](#-current-results)
-3. [🛠️ Installation & Setup](#%EF%B8%8F-installation--setup) 
-4. [🚀 Usage](#-usage)
-5. [🤝 Acknowledgements](#-acknowledgements)
-6. [💬 Citation](#-citation)
+1. [💡 简介](#-introduction)
+2. [🎯 当前成果](#-current-results)
+3. [🛠️ 安装与配置](#%EF%B8%8F-installation--setup) 
+4. [🚀 用法](#-usage)
+5. [🤝 致谢](#-acknowledgements)
+6. [💬 引用](#-citation)

-## 💡 Introduction
+## 💡 简介

-Welcome to **Agent S**, an open-source framework designed to enable autonomous interaction with computers through Agent-Computer Interface. Our mission is to build intelligent GUI agents that can learn from past experiences and perform complex tasks autonomously on your computer. 
+欢迎使用 **Agent S**，这是一个开源框架，旨在通过智能体-计算机接口（Agent-Computer Interface）实现与计算机的自主交互。我们的使命是构建智能 GUI 智能体，使其能够从过往经验中学习，并在你的计算机上自主完成复杂任务。

-Whether you're interested in AI, automation, or contributing to cutting-edge agent-based systems, we're excited to have you here!
+无论你是对 AI、自动化感兴趣，还是希望为前沿的智能体系统做贡献，我们都很高兴你能来到这里！

-## 🎯 Current Results
+## 🎯 当前成果

 <p align="center">
  <img src="images/s3_results_new.png" alt="Agent S3 Results" width="700"/>
 </p>

-On OSWorld, Agent S3 alone reaches 66% in the 100-step setting, already exceeding the previous state of the art of 63.4% (GTA1 w/ GPT-5). With the addition of Behavior Best-of-N, performance climbs even higher to 72.6%, *surpassing* human-level performance on OSWorld (~72%)!
+在 OSWorld 上，仅 Agent S3 在 100 步设置下即可达到 66%，已超过此前 63.4% 的最先进水平（GTA1 w/ GPT-5）。加入 Behavior Best-of-N 后，性能进一步提升至 72.6%，*超越* OSWorld 上的人类水平表现（约 72%）！

-Agent S3 also demonstrates strong zero-shot generalization! On WindowsAgentArena, accuracy rises from 50.2% using only Agent S3 to 56.6% by selecting from 3 rollouts. Similarly on AndroidWorld, performance improves from 68.1% to 71.6%
+Agent S3 还展现出强大的零样本泛化能力！在 WindowsAgentArena 上，仅使用 Agent S3 时准确率为 50.2%，通过从 3 次 rollout 中择优选择可提升至 56.6%。在 AndroidWorld 上，性能也从 68.1% 提升至 71.6%

-## 🛠️ Installation & Setup
+## 🛠️ 安装与配置

-### Prerequisites
- **Single Monitor**: Our agent is designed for single monitor screens
- **Security**: The agent runs Python code to control your computer - use with care
- **Supported Platforms**: Linux, Mac, and Windows
+### 前置要求
+- **单显示器**：我们的智能体面向单显示器屏幕设计
+- **安全性**：该智能体会运行 Python 代码来控制你的计算机——请谨慎使用
+- **支持的平台**：Linux、Mac 和 Windows


-### Installation
-To install Agent S3 without cloning the repository, run
+### 安装
+若无需克隆仓库即可安装 Agent S3，请运行
 ```bash
 pip install gui-agents
 ```
-If you would like to test Agent S3 while making changes, clone the repository and install using
+如果你想在修改代码的同时测试 Agent S3，请克隆仓库并按以下方式安装
 ```
 pip install -e .
 ```

-Don't forget to also `brew install tesseract`! Pytesseract requires this extra installation to work.
+别忘了还要 `brew install tesseract`！Pytesseract 需要此额外安装才能正常工作。

-### API Configuration
+### API 配置

-#### Option 1: Environment Variables
-Add to your `.bashrc` (Linux) or `.zshrc` (MacOS):
+#### 选项 1：环境变量
+添加到你的 `.bashrc`（Linux）或 `.zshrc`（MacOS）：
 ```bash
 export OPENAI_API_KEY=<YOUR_API_KEY>
 export ANTHROPIC_API_KEY=<YOUR_ANTHROPIC_API_KEY>
 export HF_TOKEN=<YOUR_HF_TOKEN>
 ```

-#### Option 2: Python Script
+#### 选项 2：Python 脚本
 ```python
 import os
 os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>"
 ```

-### Supported Models
-We support Azure OpenAI, Anthropic, Gemini, Open Router, and vLLM inference. See [models.md](models.md) for details.
+### 支持的模型
+我们支持 Azure OpenAI、Anthropic、Gemini、Open Router 和 vLLM 推理。详见 [models.md](models.md)。

-### Grounding Models (Required)
-For optimal performance, we recommend [UI-TARS-1.5-7B](https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B) hosted on Hugging Face Inference Endpoints or another provider. See [Hugging Face Inference Endpoints](https://huggingface.co/learn/cookbook/en/enterprise_dedicated_endpoints) for setup instructions.
+### 定位模型（必需）
+为获得最佳性能，我们推荐使用托管在 Hugging Face Inference Endpoints 或其他提供商上的 [UI-TARS-1.5-7B](https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B)。设置说明请参阅 [Hugging Face Inference Endpoints](https://huggingface.co/learn/cookbook/en/enterprise_dedicated_endpoints)。

-## 🚀 Usage
+## 🚀 用法


-> ⚡️ **Recommended Setup:**  
-> For the best configuration, we recommend using **OpenAI gpt-5-2025-08-07** as the main model, paired with **UI-TARS-1.5-7B** for grounding.  
+> ⚡️ **推荐配置：**  
+> 为获得最佳配置，我们建议使用 **OpenAI gpt-5-2025-08-07** 作为主模型，并搭配 **UI-TARS-1.5-7B** 进行 grounding。  


 ### CLI

-Note, this is running Agent S3, our improved agent, without bBoN. 
+请注意，这里运行的是我们改进后的 Agent S3，未使用 bBoN。

-Run Agent S3 with the required parameters:
+使用必需参数运行 Agent S3：

 ```bash
 agent_s \
@@ -161,8 +167,8 @@ agent_s \
    --grounding_height 1080
 ```

-#### Local Coding Environment (Optional)
-For tasks that require code execution (e.g., data processing, file manipulation, system automation), you can enable the local coding environment:
+#### 本地编码环境（可选）
+对于需要执行代码的任务（例如数据处理、文件操作、系统自动化），你可以启用本地编码环境：

 ```bash
 agent_s \
@@ -176,59 +182,59 @@ agent_s \
    --enable_local_env
 ```

-⚠️ **WARNING**: The local coding environment executes arbitrary Python and Bash code locally on your machine. Only use this feature in trusted environments and with trusted inputs.
+⚠️ **警告**：本地编码环境会在你的计算机上本地执行任意 Python 和 Bash 代码。请仅在受信任的环境中、并使用受信任的输入时使用此功能。

-#### Required Parameters
- **`--provider`**: Main generation model provider (e.g., openai, anthropic, etc.) - Default: "openai"
- **`--model`**: Main generation model name (e.g., gpt-5-2025-08-07) - Default: "gpt-5-2025-08-07"
- **`--ground_provider`**: The provider for the grounding model - **Required**
- **`--ground_url`**: The URL of the grounding model - **Required**
- **`--ground_model`**: The model name for the grounding model - **Required**
- **`--grounding_width`**: Width of the output coordinate resolution from the grounding model - **Required**
- **`--grounding_height`**: Height of the output coordinate resolution from the grounding model - **Required**
+#### 必需参数
+- **`--provider`**：主生成模型提供商（例如 openai、anthropic 等）- 默认值："openai"
+- **`--model`**：主生成模型名称（例如 gpt-5-2025-08-07）- 默认值："gpt-5-2025-08-07"
+- **`--ground_provider`**：接地（grounding）模型的提供商 - **必需**
+- **`--ground_url`**：接地模型的 URL - **必需**
+- **`--ground_model`**：接地模型的模型名称 - **必需**
+- **`--grounding_width`**：接地模型输出坐标分辨率的宽度 - **必需**
+- **`--grounding_height`**：接地模型输出坐标分辨率的高度 - **必需**

-#### Optional Parameters
- **`--model_temperature`**: The temperature to fix all model calls to (necessary to set to 1.0 for models like o3 but can be left blank for other models)
+#### 可选参数
+- **`--model_temperature`**：将所有模型调用固定为此温度（对于 o3 等模型需要设置为 1.0，但对于其他模型可以留空）

-#### Grounding Model Dimensions
-The grounding width and height should match the output coordinate resolution of your grounding model:
- **UI-TARS-1.5-7B**: Use `--grounding_width 1920 --grounding_height 1080`
- **UI-TARS-72B**: Use `--grounding_width 1000 --grounding_height 1000`
+#### 接地模型尺寸
+接地宽度和高度应与你的接地模型的输出坐标分辨率相匹配：
+- **UI-TARS-1.5-7B**：使用 `--grounding_width 1920 --grounding_height 1080`
+- **UI-TARS-72B**：使用 `--grounding_width 1000 --grounding_height 1000`

-#### Optional Parameters
- **`--model_url`**: Custom API URL for main generation model - Default: ""
- **`--model_api_key`**: API key for main generation model - Default: ""
- **`--ground_api_key`**: API key for grounding model endpoint - Default: ""
- **`--max_trajectory_length`**: Maximum number of image turns to keep in trajectory - Default: 8
- **`--enable_reflection`**: Enable reflection agent to assist the worker agent - Default: True
- **`--enable_local_env`**: Enable local coding environment for code execution (WARNING: Executes arbitrary code locally) - Default: False
+#### 可选参数
+- **`--model_url`**：主生成模型的自定义 API URL - 默认值：""
+- **`--model_api_key`**：主生成模型的 API 密钥 - 默认值：""
+- **`--ground_api_key`**：接地模型端点的 API 密钥 - 默认值：""
+- **`--max_trajectory_length`**：轨迹中保留的最大图像轮次数量 - 默认值：8
+- **`--enable_reflection`**：启用反思智能体（reflection agent）以辅助工作智能体 - 默认值：True
+- **`--enable_local_env`**：启用用于代码执行的本地编码环境（警告：会在本地执行任意代码）- 默认值：False

-#### Local Coding Environment Details
-The local coding environment enables Agent S3 to execute Python and Bash code directly on your machine. This is particularly useful for:
+#### 本地编码环境详情
+本地编码环境使 Agent S3 能够直接在你的计算机上执行 Python 和 Bash 代码。这对于以下场景特别有用：

- **Data Processing**: Manipulating spreadsheets, CSV files, or databases
- **File Operations**: Bulk file processing, content extraction, or file organization
- **System Automation**: Configuration changes, system setup, or automation scripts
- **Code Development**: Writing, editing, or executing code files
- **Text Processing**: Document manipulation, content editing, or formatting
+- **数据处理**：操作电子表格、CSV 文件或数据库
+- **文件操作**：批量文件处理、内容提取或文件整理
+- **系统自动化**：配置更改、系统设置或自动化脚本
+- **代码开发**：编写、编辑或执行代码文件
+- **文本处理**：文档操作、内容编辑或格式化

-When enabled, the agent can use the `call_code_agent` action to execute code blocks for tasks that can be completed through programming rather than GUI interaction.
+启用后，智能体可以使用 `call_code_agent` 操作来执行代码块，以完成可通过编程而非 GUI 交互完成的任务。

-**Requirements:**
- **Python**: The same Python interpreter used to run Agent S3 (automatically detected)
- **Bash**: Available at `/bin/bash` (standard on macOS and Linux)
- **System Permissions**: The agent runs with the same permissions as the user executing it
+**要求：**
+- **Python**：用于运行 Agent S3 的同一 Python 解释器（自动检测）
+- **Bash**：位于 `/bin/bash`（在 macOS 和 Linux 上为标准配置）
+- **系统权限**：智能体以执行它的用户相同的权限运行

-**Security Considerations:**
- The local environment executes arbitrary code with the same permissions as the user running the agent
- Only enable this feature in trusted environments
- Be cautious when the agent generates code for system-level operations
- Consider running in a sandboxed environment for untrusted tasks
- Bash scripts are executed with a 30-second timeout to prevent hanging processes
+**安全注意事项：**
+- 本地环境以运行智能体的用户相同的权限执行任意代码
+- 仅在受信任的环境中启用此功能
+- 当智能体生成用于系统级操作的代码时要谨慎
+- 对于不受信任的任务，考虑在沙盒环境中运行
+- Bash 脚本以 30 秒超时执行，以防止进程挂起

 ### `gui_agents` SDK

-First, we import the necessary modules. `AgentS3` is the main agent class for Agent S3. `OSWorldACI` is our grounding agent that translates agent actions into executable python code.
+首先，我们导入必要的模块。`AgentS3` 是 Agent S3 的主智能体类。`OSWorldACI` 是我们的接地智能体，它将智能体动作翻译为可执行的 Python 代码。
 ```python
 import pyautogui
 import io
@@ -243,7 +249,7 @@ load_dotenv()
 current_platform = "linux"  # "darwin", "windows"
 ```

-Next, we define our engine parameters. `engine_params` is used for the main agent, and `engine_params_for_grounding` is for grounding. For `engine_params_for_grounding`, we support custom endpoints like HuggingFace TGI, vLLM, and Open Router.
+接下来，我们定义引擎参数。`engine_params` 用于主智能体，`engine_params_for_grounding` 用于接地。对于 `engine_params_for_grounding`，我们支持 HuggingFace TGI、vLLM 和 Open Router 等自定义端点。

 ```python
 engine_params = {
@@ -276,7 +282,7 @@ engine_params_for_grounding = {
 }
 ```

-Then, we define our grounding agent and Agent S3.
+然后，我们定义接地智能体和 Agent S3。

 ```python
 # Optional: Enable local coding environment
@@ -301,7 +307,7 @@ agent = AgentS3(
 )
 ```

-Finally, let's query the agent!
+最后，让我们查询智能体！

 ```python
 # Get screenshot.
@@ -320,15 +326,15 @@ info, action = agent.predict(instruction=instruction, observation=obs)
 exec(action[0])
 ```

-Refer to `gui_agents/s3/cli_app.py` for more details on how the inference loop works.
+有关推理循环工作原理的更多详情，请参阅 `gui_agents/s3/cli_app.py`。

 ### OSWorld

-To deploy Agent S3 in OSWorld, follow the [OSWorld Deployment instructions](osworld_setup/s3/OSWorld.md).
+要在 OSWorld 中部署 Agent S3，请遵循 [OSWorld 部署说明](osworld_setup/s3/OSWorld.md)。

-## 💬 Citations
+## 💬 引用

-If you find this codebase useful, please cite:
+如果你认为此代码库有用，请引用：

 ```
@misc{Agent-S3,
@@ -360,6 +366,6 @@ If you find this codebase useful, please cite:
 }
 ```

-## Star History
+## Star 历史

 [![Star History Chart](https://api.star-history.com/svg?repos=simular-ai/Agent-S&type=Date)](https://star-history.com/#simular-ai/Agent-S&Date)