Add example integration for Qwen-Deep-Research model

Author: denverdino

README.md

@@ -95,6 +95,7 @@ It includes **agent deployment** and **secure sandboxed tool execution**, and ca
 | **Browser Use**         | browser_use/agent_browser                             | ✅               | ❌            | Command-line browser automation using AgentScope |
 |                         | browser_use/browser_use_fullstack_runtime             | ✅               | ✅            | Full-stack browser automation with UI & sandbox  |
 | **Deep Research**       | deep_research/agent_deep_research                     | ✅               | ❌            | Multi-agent research pipeline                    |
+|                         | deep_research/agent_qwen_deep_research                | ✅               | ❌            | Simplified deep research agent                   |
 |                         | deep_research/qwen_langgraph_search_fullstack_runtime | ❌               | ✅            | Full-stack deep research app                     |
 | **Games**               | games/game_werewolves                                 | ✅               | ❌            | Multi-agent roleplay game                        |
 | **Conversational Apps** | conversational_agents/chatbot_fullstack_runtime       | ✅               | ✅            | Chatbot application with frontend/backend        |

README_zh.md

@@ -95,6 +95,7 @@ AgentScope Runtime 是一个**全面的运行时框架**，主要解决部署和
 | **浏览器相关** | browser_use/agent_browser                             | ✅             | ❌                     | 基于 AgentScope 的命令行浏览器自动化 |
 |           | browser_use/browser_use_fullstack_runtime             | ✅             | ✅                     | 带 UI 和沙盒环境的全栈浏览器自动化     |
 | **深度研究**  | deep_research/agent_deep_research                     | ✅             | ❌                     | 多 Agent 研究流程            |
+|              | deep_research/agent_qwen_deep_research                | ✅             | ❌                     | 简化的深度研究代理                   |
 |           | deep_research/qwen_langgraph_search_fullstack_runtime | ❌             | ✅                     | 全栈运行时深度研究应用             |
 | **游戏**    | games/game_werewolves                                 | ✅             | ❌                     | 多 Agent 角色扮演推理游戏        |
 | **对话应用**  | conversational_agents/chatbot_fullstack_runtime       | ✅             | ✅                     | 带前端/后端的聊天机器人            |

deep_research/agent_qwen_deep_research/README.md

@@ -0,0 +1,29 @@
+# Deep Research Agent Example with Qwen-Deep-Research Model
+
+This example shows an Agent implementation with **Qwen-Deep-Research** model using the AgentScope framework. It can break down complex problems, uses web searches to perform analysis, and generates research reports.
+
+Reference: https://www.alibabacloud.com/help/en/model-studio/qwen-deep-research
+
+## 🚀 Getting Started
+
+### Prerequisites
+
+- Python 3.10 or higher
+- DashScope API key from [Alibaba Cloud](https://dashscope.console.aliyun.com/)
+
+### Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+### Usage
+
+1. **Set Environment Variable**:
+   ```bash
+   export DASHSCOPE_API_KEY="your_dashscope_api_key_here"
+   ```
+2. **Run the script**:
+    ```bash
+   python main.py
+   ```

deep_research/agent_qwen_deep_research/main.py

@@ -0,0 +1,51 @@
+# -*- coding: utf-8 -*-
+"""The main entry point of the Qwen Deep Research agent example."""
+import asyncio
+
+from qwen_deep_research_agent import QwenDeepResearchAgent
+from agentscope import logger
+from agentscope.message import Msg
+
+
+async def main() -> None:
+    """The main entry point for the Qwen Deep Research agent example."""
+    # Create DeepResearch Agent
+    researcher = QwenDeepResearchAgent(
+        name="Researcher Qwen",
+        verbose=True,
+    )
+
+    # Step 1: Model follow-up question for confirmation
+    # The model analyzes the user's question
+    # and asks follow-up questions to clarify the research direction.
+    user_msg = Msg(
+        name="User",
+        content="Research the applications of artificial intelligence in "
+        "education",
+        role="user",
+    )
+
+    clarification = await researcher(user_msg)
+    print(f"\n{clarification.name}: {clarification.content}\n")
+
+    # Step 2: Deep research
+    # Based on the content of the follow-up question in Step 1,
+    # the model executes the complete research process.
+    user_response = Msg(
+        name="User",
+        content="I am mainly interested in personalized learning and "
+        "intelligent assessment.",
+        role="user",
+    )
+
+    research_result = await researcher(user_response)
+    print(f"\n{research_result.name}: {research_result.content}\n")
+
+    print("\n✅ Research complete!\n")
+
+
+if __name__ == "__main__":
+    try:
+        asyncio.run(main())
+    except Exception as e:
+        logger.exception(e)