evotraders/deep_research/qwen_langgraph_search_fullstack_runtime

DeepSearch Demo of Agentscope-Runtime with Langgraph / Qwen and Quark search

This project is modified from 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:

🌳 Project Structure

├── 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:

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:

pip install -r requirements.txt

Usage

Start the CLI Service.

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.

📄 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.