Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy

🏆 ACL 2025 Main Conference Paper

Junqi Gao ^1,2, Xiang Zou ², Ying Ai ³, Dong Li ^1,2,†, Yichen Niu ³, Biqing Qi ^1,†, Jianxing Liu ³

¹ Shanghai Artificial Intelligence Laboratory

² School of Mathematics, Harbin Institute of Technology

³ Department of Control Science and Engineering, Harbin Institute of Technology

^† Corresponding Authors

✨ Key Features

🧠 Multi-Agent Synergy: Planning, Thought, and Execution agents for optimized reasoning

🌐 Adaptive Graph Exploration: Dynamic retrieval strategies for complex knowledge graphs

🔍 Self-Reflection: Multi-perspective analysis for improved accuracy

⚙️ Installation

conda create -n graphcounselor python=3.8.1
conda activate graphcounselor
conda install pytorch1.12.1 torchvision0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
conda install -c pytorch -c nvidia faiss-gpu=1.7.4
conda install -c conda-forge langchain0.1.0 langchain-core0.1.7 langchain-community==0.0.9
conda install -c conda-forge openai1.6.1 scikit-learn1.3.2 sentence-transformers==2.2.2
conda install -c conda-forge transformers4.36.2 datasets2.16.1
conda install jsonlines tiktoken networkx IPython
pip install evaluate absl-py rouge_score

🚀 Quick Start

1. Download graph data here and save to data/processed_data/{data_name}

2. Run Graph Counselor: bash scripts/run_Graph-Counselor.sh

3. Evaluation: bash eval.sh

📚 Citation

@article{gao2025graphcounselor,
  title={Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy},
  author={Junqi Gao and Xiang Zou and Ying Ai and Dong Li and Yichen Niu and Biqing Qi and Jianxing Liu},
  journal={arXiv preprint arXiv:2506.03939},
  year={2025},
  url={https://arxiv.org/abs/2506.03939}
}