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

TL;DR

This paper introduces Magentic-Marketplace, a simulated environment for studying agentic markets involving large language model agents, revealing insights into market dynamics, behaviors, and the impact of search mechanisms.

Contribution

It presents a novel open-source environment for analyzing complex agent interactions in realistic markets, addressing limitations of prior constrained studies.

Findings

Frontier models approach optimal welfare under ideal search conditions

Performance drops significantly as market scale increases

Severe first-proposal bias favors response speed over quality

Abstract

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.