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+# DeepSearch Demo of Agentscope-Runtime with Langgraph / Qwen and Quark search
+This project is modified from [Gemini Fullstack LangGraph Quickstart](https://github.com/google-gemini/gemini-fullstack-langgraph-quickstart).
+
+It contains following key features:
+1. We use LangGraph to build an agent (directed state graph) with the help of Qwen and Quark search.
+2. The agent is wrapped as an Agentscope-Runtime agent and deployed as a service.
+3. The interaction with the agent is done through a simple CLI.
+
+Click following image to watch the video demo:
+
+[![watch_the_video](https://img.alicdn.com/imgextra/i3/6000000000386/O1CN01vDit5y1EipxsRceBd_!!6000000000386-0-tbvideo.jpg)](https://cloud.video.taobao.com/vod/-BhtPfhYZv8pCz7L1vYmKCDtf1QEaDXNX1hMnvj_BUQ.mp4)
+<br />
+
+## 🌳 Project Structure
+```bash
+├── src                               # Source code directory containing the core functionalities and modules
+│   ├── init.py                       # Initialization script, possibly setting up environment or configurations
+│   ├── configuration.py              # Module for handling application configurations and settings
+│   ├── custom_search_tool.py         # Implements custom search functionality or tool
+│   ├── graph_openai_compatible.py    # Module for OpenAI-compatible graph operations or integrations
+│   ├── llm_prompts.py                # Contains large language model prompts used in the application
+│   ├── llm_utils.py                  # Utility functions for handling large language model operations
+│   ├── main.py                       # Main entry script to launch or execute the application
+│   ├── state.py                      # Manages application state or data persistence
+│   ├── tools_and_schemas.py          # Defines various tools and data schemas used by the application
+│   └── utils.py                      # General utility functions used across the application
+└── README.md                         # Project documentation file providing information and usage instructions
+```
+
+## Architecture
+The architecture of the demo is shown in the following diagram:
+```mermaid
+graph LR;
+    subgraph As["AgentScope Runtime"]
+        F[Agent Engine]
+      end
+    subgraph Bs["LangGraph"]
+        B1[Web Research]
+        B2[Reflection]
+        B3[Answer Generation]
+        B0[Generation Queries]
+        B0 --> B1
+        B1 --> B2
+        B2 --> |if insufficient| B1
+        B2 --> |if sufficient| B3
+    end
+    subgraph Cs["CLI Service"]
+        C[main] --> S[WebSearchGraph]
+        end
+    As --> Bs
+
+    user --> |user input| C
+    S--> F
+
+```
+
+## 📖 Overview
+
+This demo demonstrates how to build a sophisticated research agent using:
+- Qwen as the underlying language model
+- LangGraph for defining complex agent workflows
+- Custom search tools for information retrieval
+- State management for multi-step reasoning
+
+The implementation showcases advanced patterns for building agentic systems that can perform deep research tasks through iterative thinking and tool usage.
+
+## ⚙️ Components
+
+### Core Modules
+- `configuration.py`: Configuration management for the agent
+- `custom_search_tool.py`: Custom search functionality implementation
+- `graph_openai_compatible.py`: LangGraph implementation with OpenAI compatibility
+- `llm_prompts.py`: Prompt templates for different agent behaviors
+- `llm_utils.py`: Utility functions for LLM interactions
+- `main.py`: Main entry point for the application
+- `state.py`: State management for the LangGraph workflow
+- `tools_and_schemas.py`: Tool definitions and data schemas
+- `utils.py`: General utility functions
+
+## 🚀 Getting Started
+
+
+## Install
+
+Follow these steps to get the application running locally for development and testing.
+
+**1. Prerequisites:**
+
+-   Python 3.11+
+-   Create a file named `.env` by copying the `.env.example` file.
+-   **`DASHSCOPE_API_KEY`**:
+  add  `DASHSCOPE_API_KEY="YOUR_ACTUAL_API_KEY"` if you use dashscope API.
+- Quark Search API KEY: add `QUARK_AK=''` and `QUARK_SK=''` to `.env` file if you use quark search API.
+
+
+**2. Install Dependencies:**
+
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+Start the CLI Service.
+```bash
+cd src
+python main.py
+```
+After that you can use the CLI to interact with the agent.
+
+## 🛠️ Features
+
+- `qwen` integration for advanced language understanding
+- `langgraph` for complex workflow management
+- Custom search tools for information retrieval
+- Multi-step reasoning capabilities
+- Stateful agent interactions
+- Research-focused agent workflows
+
+## ℹ️ Getting help
+
+If you have any questions or if you found any problems with this demo, please report through [GitHub issues](https://github.com/your-org/demohouse/issues).
+
+## 📄 License
+
+This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.