# InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains

arXiv: https://arxiv.org/abs/2407.11384 · HTML: https://mlsanity.com/paper/2407.11384/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?](https://mlsanity.com/paper/2011.09533/) (2020)
- [OR-Gym: A Reinforcement Learning Library for Operations Research Problems](https://mlsanity.com/paper/2008.06319/) (2020)
- [EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning](https://mlsanity.com/paper/2405.07938/) (2024)
- [An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems](https://mlsanity.com/paper/2307.11432/) (2023)
- [An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failures](https://mlsanity.com/paper/2308.04898/) (2023)

## Built on by (most cited first, 22 of 22)

- [Do as We Do, Not as You Think: the Conformity of Large Language Models](https://mlsanity.com/paper/2501.13381/) (2025)
- [Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking](https://mlsanity.com/paper/2411.10184/) (2024)
- [Ask, Clarify, Optimize: Human-LLM Agent Collaboration for Smarter Inventory Control](https://mlsanity.com/paper/2601.00121/) (2025)
- [Rethinking Supply Chain Planning: A Generative Paradigm](https://mlsanity.com/paper/2509.03811/) (2025)
- [Leveraging Large Language Models for Risk Assessment in Hyperconnected Logistic Hub Network Deployment](https://mlsanity.com/paper/2503.21115/) (2025)
- [LLMs for Supply Chain Management](https://mlsanity.com/paper/2505.18597/) (2025)
- [AutoBnB-RAG: Enhancing Multi-Agent Incident Response with Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.13118/) (2025)
- [Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management](https://mlsanity.com/paper/2605.17036/) (2026)
- [Training-Free Multimodal Large Language Model Orchestration](https://mlsanity.com/paper/2508.10016/) (2025)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [Agentic AI Sustainability Assessment for Supply Chain Document Insights](https://mlsanity.com/paper/2511.07097/) (2025)
- [Automating Supply Chain Disruption Monitoring via an Agentic AI Approach](https://mlsanity.com/paper/2601.09680/) (2026)
- [Contract2Plan: Verified Contract-Grounded Retrieval-Augmented Optimization for BOM-Aware Procurement and Multi-Echelon Inventory Planning](https://mlsanity.com/paper/2601.06164/) (2026)
- [InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees](https://mlsanity.com/paper/2605.00369/) (2026)
- [Helicase: Uncertainty-Guided Supply Chain Knowledge Graph Construction with Autonomous Multi-Agent LLMs](https://mlsanity.com/paper/2605.26835/) (2026)
- [STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oracle](https://mlsanity.com/paper/2607.13618/) (2026)
- [EconSkills: Studying Skill Transfer and Retrieval for Web Agents on Live Economic Data](https://mlsanity.com/paper/2609.19523/) (2026)
- [AgentBnB: A Browser-Based Cybersecurity Tabletop Exercise with Large Language Model Support and Retrieval-Aligned Scaffolding](https://mlsanity.com/paper/2511.00265/) (2025)
- [SupplyNet: Supporting Visual Exploratory Learning in Supply Chain via Contextual Multi-Agent Simulation](https://mlsanity.com/paper/2606.24694/) (2026)
- [Semantic Signal-Assisted Inspection and Recovery Allocation in Reverse Logistics](https://mlsanity.com/paper/2609.02116/) (2026)
- [A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics](https://mlsanity.com/paper/2609.13561/) (2026)
- [AutoMARS: Automatic and Scalable Multi-Agent Large Language Model Framework for Supply Chain Risk Assessment](https://mlsanity.com/paper/2610.17206/) (2026)

## Abstract (arXiv)

Supply chain management (SCM) involves coordinating the flow of goods, information, and finances across various entities to deliver products efficiently. Effective inventory management is crucial in today's volatile and uncertain world. Previous research has demonstrated the superiority of heuristic methods and reinforcement learning applications in inventory management. However, the application of large language models (LLMs) as autonomous agents in multi-agent systems for inventory management remains underexplored. This study introduces a novel approach using LLMs to manage multi-agent inventory systems. Leveraging their zero-shot learning capabilities, our model, InvAgent, enhances resilience and improves efficiency across the supply chain network. Our contributions include utilizing LLMs for zero-shot learning to enable adaptive and informed decision-making without prior training, providing explainability and clarity through chain-of-thought, and demonstrating dynamic adaptability to varying demand scenarios while reducing costs and preventing stockouts. Extensive evaluations across different scenarios highlight the efficiency of our model in SCM.