A Technical Taxonomy of LLM Agent Communication Protocols

2026-06-17

AI Summary

This study addresses the severe fragmentation of large language model (LLM) communication protocols in multi-agent systems, which significantly hinders interoperability. The work proposes the first structured taxonomy specifically tailored to LLM agent communication protocols, developed through an empirical-conceptual bidirectional iterative approach. By systematically analyzing nine prominent open-source protocols, the authors derive a five-dimensional classification framework encompassing communication parties, payload structure, interaction state, discovery mechanisms, and pattern flexibility. The analysis identifies recurring architectural patterns—including hybrid payloads, persistent conversation states, and runtime protocol negotiation—revealing commonalities and evolutionary trends across existing protocols. The study further forecasts a trajectory toward federated, layered protocol stacks and highlights critical research gaps, particularly in privacy preservation and policy enforcement, thereby offering theoretical guidance for future protocol design and selection.

Abstract

As large language models (LLMs) advance and multi-agent systems aim to overcome the limits of standalone agents, robust communication protocols are becoming essential infrastructure for distributed agent networks. Nonetheless, the fragmented protocol landscape presents a significant interoperability challenge. This study develops a technical taxonomy to classify and analyze LLM agent communication protocols. Following an established iterative method, we defined the taxonomy's purpose, meta-characteristic, and ending conditions, then performed five iterations, three empirical-to-conceptual and two conceptual-to-empirical, on nine actively maintained open-source protocols with demonstrable adoption. The taxonomy comprises five dimensions: counterparty, payload, interaction state, discovery mechanism, and schema flexibility. Classification reveals recurring architectural patterns: all sampled agent-to-agent protocols combine hybrid payloads with session-state persistence; most protocols support multiple predefined schemas, and two negotiate schemas at runtime, indicating a trend toward schema flexibility; decentralized discovery remains rare. Analysis suggests short-term convergence pressure toward protocols unifying agent-to-agent and agent-to-context (tool and data) communication. Long-term, however, no single protocol is likely to maximize versatility, efficiency, and portability simultaneously. The field will more likely evolve toward a federated, layered protocol stack. The framework guides protocol selection and highlights open research gaps such as privacy and policy enforcement.}

Problem

• LLM agent communication
• interoperability
• protocol taxonomy
• multi-agent systems
• communication protocols

Innovation

• LLM agent communication
• technical taxonomy
• protocol interoperability
• schema flexibility
• federated protocol stack