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deep_research/agent_deep_research/README.md

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+# Deep Research Agent Example
+
+## What This Example Demonstrates
+
+This example shows a **DeepResearch Agent** implementation using the AgentScope framework. The DeepResearch Agent specializes in performing multi-step research to collect and integrate information from multiple sources, and generates comprehensive reports to solve complex tasks.
+## Prerequisites
+
+- Python 3.10 or higher
+- Node.js and npm (for the MCP server)
+- DashScope API key from [Alibaba Cloud](https://dashscope.console.aliyun.com/)
+- Tavily search API key from [Tavily](https://www.tavily.com/)
+
+## How to Run This Example
+1. **Set Environment Variable**:
+   ```bash
+   export DASHSCOPE_API_KEY="your_dashscope_api_key_here"
+   export TAVILY_API_KEY="your_tavily_api_key_here"
+   export AGENT_OPERATION_DIR="your_own_direction_here"
+   ```
+2. **Test Tavily MCP Server**:
+    ```bash
+    npx -y tavily-mcp@latest
+    ```
+
+2. **Run the script**:
+    ```bash
+   python main.py
+   ```
+
+## Connect to Web Search MCP client
+The DeepResearch Agent only supports web search through the Tavily MCP client currently. To use this feature, you need to start the MCP server locally and establish a connection to it.
+```
+from agentscope.mcp import StdIOStatefulClient
+
+tavily_search_client= StdIOStatefulClient(
+    name="tavily_mcp",
+    command="npx",
+    args=["-y", "tavily-mcp@latest"],
+    env={"TAVILY_API_KEY": os.getenv("TAVILY_API_KEY", "")},
+)
+await tavily_search_client.connect()
+```
+
+> Note: The example is built with DashScope chat model. If you want to change the model in this example, don't forget
+> to change the formatter at the same time! The corresponding relationship between built-in models and formatters are
+> list in [our tutorial](https://doc.agentscope.io/tutorial/task_prompt.html#id1)