# Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets

arXiv: https://arxiv.org/abs/2510.25779 · HTML: https://mlsanity.com/paper/2510.25779/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 44 of its 57 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 30 of 44)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [YaRN: Efficient Context Window Extension of Large Language Models](https://mlsanity.com/paper/2309.00071/) (2023)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://mlsanity.com/paper/1706.05125/) (2017)
- [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025)
- [Decoupling Strategy and Generation in Negotiation Dialogues](https://mlsanity.com/paper/1808.09637/) (2018)
- [EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities](https://mlsanity.com/paper/2310.10436/) (2023)
- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [GUI Agents with Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2411.04890/) (2024)
- [A Survey on Code Generation with LLM-based Agents](https://mlsanity.com/paper/2508.00083/) (2025)
- [How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis](https://mlsanity.com/paper/2402.05863/) (2024)
- [SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning](https://mlsanity.com/paper/2506.24119/) (2025)
- [Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation](https://mlsanity.com/paper/2309.17234/) (2023)
- [A Survey of AI Agent Protocols](https://mlsanity.com/paper/2504.16736/) (2025)
- [A Self-Improving Coding Agent](https://mlsanity.com/paper/2504.15228/) (2025)
- [Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena](https://mlsanity.com/paper/2310.05746/) (2023)
- [The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://mlsanity.com/paper/2004.13332/) (2020)
- [CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments](https://mlsanity.com/paper/2411.02305/) (2024)
- [Algorithmic Collusion by Large Language Models](https://mlsanity.com/paper/2404.00806/) (2024)
- [Determinants of LLM-assisted Decision-Making](https://mlsanity.com/paper/2402.17385/) (2024)
- [Game-theoretic LLM: Agent Workflow for Negotiation Games](https://mlsanity.com/paper/2411.05990/) (2024)
- [STEER: Assessing the Economic Rationality of Large Language Models](https://mlsanity.com/paper/2402.09552/) (2024)
- [Agent Network Protocol Technical White Paper](https://mlsanity.com/paper/2508.00007/) (2025)
- [STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making](https://mlsanity.com/paper/2405.16376/) (2024)

## Built on by (most cited first, 19 of 19)

- [Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents](https://mlsanity.com/paper/2606.05391/) (2026)
- [Auditing Preferences for Brands and Cultures in LLMs](https://mlsanity.com/paper/2603.18300/) (2026)
- [What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents](https://mlsanity.com/paper/2604.04468/) (2026)
- [Emergent Misaligned Communication in Long-Horizon Multi-Agent LLM Commerce](https://mlsanity.com/paper/2608.14825/) (2026)
- [Markets, Not Planners: Decentralized Orchestration of LLM Agents with Private Information](https://mlsanity.com/paper/2608.23867/) (2026)
- [When Guardrails Look Effective: Construct Validity Failures in LLM Agent Commerce Evaluation](https://mlsanity.com/paper/2609.01519/) (2026)
- [Reasonably reasoning AI agents can avoid game-theoretic failures in zero-shot, provably](https://mlsanity.com/paper/2603.18563/) (2026)
- [SovereignNegotiation-Bench: Evaluating User-Owned Personal Agents In Delegated Bargaining Under Privacy, Consent, Evidence, And Institutional Pressure](https://mlsanity.com/paper/2607.02814/) (2026)
- [Paying for Honesty Without Knowing the Truth: Reputation-Penalty Design for LLM Marketplace Agents](https://mlsanity.com/paper/2607.28330/) (2026)
- [Agentic Commerce World: An Auditable and Verifiable Environment for Vibe Commerce](https://mlsanity.com/paper/2608.02441/) (2026)
- [TourMart: A Parametric Audit Instrument for Commission Steering in LLM Travel Agents](https://mlsanity.com/paper/2605.10440/) (2026)
- [REDDIT: Forgetting-Resistant Correction of Timestamp Drift in ASR via Replay-Based Distribution Editing](https://mlsanity.com/paper/2607.05364/) (2026)
- [Right-Sizing LLM-Agent Decomposition in VAT Determination: A Pilot Controlled Sweep](https://mlsanity.com/paper/2608.23395/) (2026)
- [CAVEAT: Towards Robust Computer-Use Agents in Incentive-Misaligned Environments](https://mlsanity.com/paper/2609.27273/) (2026)
- [Worse Together: How Performance Breaks Down in Multi-User Multi-Agent Teams](https://mlsanity.com/paper/2610.00583/) (2026)
- [Do Small Language Models Learn to Negotiate? A Controlled Scaling Study of RL-Trained Sellers](https://mlsanity.com/paper/2610.06204/) (2026)
- [BazaarBench: Delegation Safety in Decentralized C2C Marketplaces Run by LLM Agents](https://mlsanity.com/paper/2610.06748/) (2026)
- [A Society of Researchers: Designing Institutions for Populations of Autonomous Research Agents](https://mlsanity.com/paper/2610.10468/) (2026)
- [Get In, Decide, Prove: A Merchant-Side Readiness Benchmark for Agentic Commerce](https://mlsanity.com/paper/2610.17378/) (2026)

## Abstract (arXiv)

As LLM agents advance, they are increasingly mediating economic decisions, ranging from product discovery to transactions, on behalf of users. Such applications promise benefits but also raise many questions about agent accountability and value for users. Addressing these questions requires understanding how agents behave in realistic market conditions. However, previous research has largely evaluated agents in constrained settings, such as single-task marketplaces (e.g., negotiation) or structured two-agent interactions. Real-world markets are fundamentally different: they require agents to handle diverse economic activities and coordinate within large, dynamic ecosystems where multiple agents with opaque behaviors may engage in open-ended dialogues. To bridge this gap, we investigate two-sided agentic marketplaces where Assistant agents represent consumers and Service agents represent competing businesses. To study these interactions safely, we develop Magentic-Marketplace -- a simulated environment where Assistants and Services can operate. This environment enables us to study key market dynamics: the utility agents achieve, behavioral biases, vulnerability to manipulation, and how search mechanisms shape market outcomes. Our experiments show that frontier models can approach optimal welfare -- but only under ideal search conditions. Performance degrades sharply with scale, and all models exhibit severe first-proposal bias, creating 10-30x advantages for response speed over quality. These findings reveal how behaviors emerge across market conditions, informing the design of fair and efficient agentic marketplaces.