LNR Server 01: Input Data Processing

📣 Important Notice

__⚠️ As the paper is under review, all contents in this repository are currently not permitted for reuse by anyone until this announcement is removed. Thank you for your understanding! 🙏__

1. Overview & Objectives

This repository contains the complete implementation, experimental data, and supplementary results for the paper ××× developed by XXX University in China, and .

Pending publication, the code is shared under a restrictive license. Once the paper is accepted, the repository will transition to a MIT license. Please contact the corresponding author for any inquiries regarding academic use during the review period.

2. Videos of agents operation

2.1 Operation of the developed prototype

↓↓↓ A demonstration of using the developed prototype to operate the TCG-TE LNR agents using graph-guided MCP tools

<video src="https://github.com/user-attachments/assets/62ce60a8-4f43-4ff6-a787-aa9784b2f03a" width="880"></video>

The full video could be found here

↓↓↓ A demonstration of using the developed prototype to integrate a new MCP server to TCG-TE LNR agents

<video src="https://github.com/user-attachments/assets/e82f1150-2a8f-474f-b5c4-1dd9c0fcb57c" width="880"></video>

The full video could be found here

2.2 Operation of agents based on NPG-TE pattern

↓↓↓ A snippet of the operation of NPG-TE agent with discrete MCP tools driven by GPT-5.

<video src="https://github.com/user-attachments/assets/5c7c539d-9b38-4b55-abbd-5fe7da966d7c" width="880"></video>

↓↓↓ A screenshot of Agent's response

The full video can be found here

↓↓↓ A snippet of the operation of NPG-TE agents with discrete MCP tools driven by GPT-4o.

<video src="https://github.com/user-attachments/assets/040dbadc-c25b-461a-9bba-7391168058cb" width="880"></video>

↓↓↓ A screenshot of Agent's response

The full video can be found here

2.3 Operation of agents based on TCG-TE pattern

↓↓↓ A snippet of the operation of TCG-TE agents with graph-guided MCP tools driven by Claude sonnet 3.7.

The full video can be found here

↓↓↓ A snippet of the operation of TCG-TE agents with graph-guided MCP tools driven by GPT-4.1.

<video src="https://github.com/user-attachments/assets/8159ea48-1421-4158-b067-1bdcd8dd531e" width="880"></video>

↓↓↓ A screenshot of Agent's response

The full video can be found here

3. Repository Structure

4. Acknowledgments

This work heavily relies on excellent open-source projects, including but not limited to:
- LangGraph & LangChain
- Hugging Face MTEB leaderboard
- NetworkX, PyTorch Geometric, and numerous LLM providers (OpenAI, Anthropic, Qwen, Llama, etc.)

We are deeply grateful to all contributors of these foundational work.