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 snippet of using the **developed prototype** to run the TS-ReAct-based agents driven by GPT-4o ↓↓↓ A snippet of **updating the tool kit in the prototype** The full video to showcase the prototype and tool kit updating can be found in: ### 2.2 Operation of agents based on ReAct pattern ↓↓↓ A snippet of running the **ReAct-based agents driven by GPT-4o, GPT-4, and GPT-3.5 Turbo**. The full video can be found here () ↓↓↓ A snippet of running the **ReAct-based agents driven by Qwen2.5, Deepseek-V3, Gemma-2, Llama-3.1, and Mixtral MoE**. The full video can be found here () ### 2.3 Operation of agents based on TS-ReAct pattern ↓↓↓ A snippet of running the **TS agent based on TS-ReAct pattern**. The full video can be found here () ↓↓↓ A snippet of running the **ReAct agent based on TS-ReAct pattern**. 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. ## 5. How to Reuse This Repository ### 5.1 Importing the Lifeline Recovery Tool Set 1. Copy all tool definition files from `tools/` into your target agent directory. 2. Import the tools using the standardized registry pattern shown in the example notebooks. ### 5.2 Running Baseline ReAct Agents - Directory: `agents_reAct/` - Supports 8 different LLMs (GPT-4o, Claude-3, Llama-3.1-405B, Qwen2.5, etc.) - Ready-to-run scripts with configuration YAMLs ### 5.3 Running the Proposed GraphRAG + MCP Agents - Directory: `agents_graphRAG_MCP/` - Same 8 backbone LLMs - Includes GraphRAG index construction scripts and MCP search configurations ### 5.4 Running the Interactive Prototype - Directory: `prototype/` - Dynamic tool registration/hot-reloading - Web-based GUI + terminal interface - Supports on-the-fly addition of new recovery actions