{"id":23497,"date":"2012-06-24T12:27:12","date_gmt":"2012-06-24T12:27:12","guid":{"rendered":"https:\/\/scannn.com\/deep-jlu-awesome-graph-engineering-a-survey-on-graph-engineering-in-the-era-of-llm-agents-from-individual-intelligence-to-system-intelligence-%c2%b7-github\/"},"modified":"2012-06-24T12:27:12","modified_gmt":"2012-06-24T12:27:12","slug":"deep-jlu-awesome-graph-engineering-a-survey-on-graph-engineering-in-the-era-of-llm-agents-from-individual-intelligence-to-system-intelligence-%c2%b7-github","status":"publish","type":"post","link":"https:\/\/scannn.com\/lv\/deep-jlu-awesome-graph-engineering-a-survey-on-graph-engineering-in-the-era-of-llm-agents-from-individual-intelligence-to-system-intelligence-%c2%b7-github\/","title":{"rendered":"DEEP-JLU\/Awesome-Graph-Engineering: A Survey on Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence \u00b7 GitHub"},"content":{"rendered":"\n
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A curated collection of research papers, benchmarks, and open-source projects on Graph Engineering in the era of LLM Agents<\/strong>. This repository accompanies the survey Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence<\/a><\/em> and will be continuously updated.<\/p>\n

Graph Engineering studies how explicit, dynamic, and evolving graph structures can organize tasks, coordinate heterogeneous agents, maintain runtime state, and support system evolution. The collection follows the survey’s progression from Model Intelligence<\/strong>, through Individual Intelligence<\/strong>, to System Intelligence<\/strong>.<\/p>\n

Contributions are welcome.<\/strong> If you find a missing resource or a relevant new work, please open an issue or submit a pull request.<\/p>\n

Please cite our paper<\/a><\/strong> if you find this survey or repository helpful.<\/p>\n

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@misc<\/span>{feng2026graphengineeringerallm<\/span>,\n      title<\/span>={<\/span>Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence}<\/span><\/span>,\n      author<\/span>={<\/span>Yuyuan Feng and Zhishang Xiang and Chaobin Yang and Qichao Ma and Zerui Chen and Yujing Zhang and Ke Huang and Chuanjie Wu and Zhaoxu Liu and Yili Wang and Xin He and Jiapu Wang and Zijin Hong and Hao Chen and Yuanchen Bei and Kun Wang and Shengyuan Chen and Ningyu Zhang and Enyan Dai and Linhao Luo and Qingyi Pan and Qi Wang and Wenqi Fan and Guangjing Wang and Na Zou and Yangqiu Song and Xin Wang and Zechao Li and Xia Hu and Qing Li and Xiao Huang and Zhihong Zhang and Jinsong Su and Qinggang Zhang and Yi Chang}<\/span><\/span>,\n      year<\/span>={<\/span>2026}<\/span><\/span>,\n      eprint<\/span>={<\/span>2608.21156}<\/span><\/span>,\n      archivePrefix<\/span>={<\/span>arXiv}<\/span><\/span>,\n      primaryClass<\/span>={<\/span>cs.IR}<\/span><\/span>,\n      url<\/span>={<\/span>https:\/\/arxiv.org\/abs\/2608.21156}<\/span><\/span>,\n}<\/pre>\n<\/div>\n\n
Graph Engineering provides a structured path from standalone model capability to coordinated system-level intelligence:<\/p>\n