Tool use enables large language models (LLMs) to access external information, invoke software systems, and act in digital environments beyond what can be solved from model parameters alone. Early research mainly studied whether a model could select and execute a correct single tool call. As agent systems evolve, however, the central problem has shifted from isolated invocation to multi-tool orchestration over long trajectories with intermediate state, execution feedback, changing environments, and practical constraints such as safety, cost, and verifiability. We comprehensively review recent progress in multi-tool LLM agents and analyzes the state of the art in this rapidly developing area. First, we unify task formulations and distinguish single-call tool use from long-horizon orchestration. Then, we organize the literature around six core dimensions: inference-time planning and execution, training and trajectory construction, safety and control, efficiency under resource constraints, capability completeness in open environments, and benchmark design and evaluation. We further summarize representative applications in software engineering, enterprise workflows, graphical user interfaces, and mobile systems. Finally, we discuss major challenges and outline future directions for building reliable, scalable, and verifiable multi-tool agents.
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；The Chinese University of Hong Kong Shenzhen（香港中文大学（深圳））；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research
Om AI Research（Om AI研究机构）；Binjiang Institute of Zhejiang University（浙江大学滨江学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
SwarmBench: Can Large Language Models Act as Agent Swarm Orchestrators?
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Multi-Agent Collaboration via Cross-Team Orchestration
State Key Lab of CAD & CG（CAD与CG国家重点实验室）；Zhejiang Polytechnic Institute（浙江工业大学）；Polytechnic Institute, Zhejiang University（浙江大学 polytechnic 院）；Shanghai Jiao Tong University（上海交通大学）；King’s College London（伦敦国王学院）；Tsinghua University（清华大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Tencent Robotics X（腾讯机器人科技）
Multi-Agent Collaboration via Evolving Orchestration
Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Siemens（西门子）；Tencent Robotics X（腾讯机器人研究院）
From Single Chatbots to Governed Agent Ecosystems: An Agentic AI Pattern Catalogue and Orchestration Framework for Mission-Critical Hospital Information Management Systems
Benchmarking Multi-Agent LLM Architectures for Financial Document Processing: A Comparative Study of Orchestration Patterns, Cost-Accuracy Tradeoffs and Production Scaling Strategies
Department of Computer Science and Engineering（计算机科学与工程系）；New York University（纽约大学）；New York, NY 10012（纽约市NY 10012）
MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Alibaba Group（阿里巴巴集团）
DUCTILE: Agentic LLM Orchestration of Engineering Analysis in Product Development Practice
Product Development Division, Department of Mechanical Engineering, Chalmers University of Technology, Gothenburg, Sweden（产品开发部门，机械工程系，楚姆勒斯技术大学，哥德堡，瑞典）；GKN Aerospace Engine Systems, Trollhättan, Sweden（GKN航空发动机系统，特罗尔哈塔纳，瑞典）
