# A Survey of AI Agent Protocols

arXiv: https://arxiv.org/abs/2504.16736 · HTML: https://mlsanity.com/paper/2504.16736/

## Key facts

- Cited by 81 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 23 of its 50 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 23 of 23)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [Tool Learning with Large Language Models: A Survey](https://mlsanity.com/paper/2405.17935/) (2024)
- [How Can Recommender Systems Benefit from Large Language Models: A Survey](https://mlsanity.com/paper/2306.05817/) (2023)
- [Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence](https://mlsanity.com/paper/2407.07061/) (2024)
- [Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generation](https://mlsanity.com/paper/2308.10335/) (2023)
- [Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks](https://mlsanity.com/paper/2205.05718/) (2022)
- [A Scalable Communication Protocol for Networks of Large Language Models](https://mlsanity.com/paper/2410.11905/) (2024)
- [TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision](https://mlsanity.com/paper/2403.06221/) (2024)
- [Agentic Information Retrieval](https://mlsanity.com/paper/2410.09713/) (2024)
- [LOKA Protocol: A Decentralized Framework for Trustworthy and Ethical AI Agent Ecosystems](https://mlsanity.com/paper/2504.10915/) (2025)
- [Understanding and Optimizing Agentic Workflows via Shapley value](https://mlsanity.com/paper/2502.00510/) (2025)
- [Low-latency Networking: Where Latency Lurks and How to Tame It](https://mlsanity.com/paper/1808.02079/) (2018)
- [Multi-Turn Human-LLM Interaction Through the Lens of a Two-Way Intelligibility Protocol](https://mlsanity.com/paper/2410.20600/) (2024)

## Built on by (most cited first, 30 of 81)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering](https://mlsanity.com/paper/2604.08224/) (2026)
- [From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows](https://mlsanity.com/paper/2506.23260/) (2025)
- [A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures](https://mlsanity.com/paper/2506.19676/) (2025)
- [LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions](https://mlsanity.com/paper/2509.18970/) (2025)
- [Agentic Web: Weaving the Next Web with AI Agents](https://mlsanity.com/paper/2507.21206/) (2025)
- [A Survey of Process Reward Models: From Outcome Signals to Process Supervisions for Large Language Models](https://mlsanity.com/paper/2510.08049/) (2025)
- [The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems](https://mlsanity.com/paper/2602.17753/) (2026)
- [StepORLM: A Self-Evolving Framework With Generative Process Supervision For Operations Research Language Models](https://mlsanity.com/paper/2509.22558/) (2025)
- [Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities](https://mlsanity.com/paper/2506.18019/) (2025)
- [AI Scientists Fail Without Strong Implementation Capability](https://mlsanity.com/paper/2506.01372/) (2025)
- [Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets](https://mlsanity.com/paper/2510.25779/) (2025)
- [Agentic AI Frameworks: Architectures, Protocols, and Design Challenges](https://mlsanity.com/paper/2508.10146/) (2025)
- [InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.15872/) (2025)
- [Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP](https://mlsanity.com/paper/2602.11327/) (2026)
- [From Glue-Code to Protocols: A Critical Analysis of A2A and MCP Integration for Scalable Agent Systems](https://mlsanity.com/paper/2505.03864/) (2025)
- [Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agents](https://mlsanity.com/paper/2601.10955/) (2026)
- [AgentMaster: A Multi-Agent Conversational Framework Using A2A and MCP Protocols for Multimodal Information Retrieval and Analysis](https://mlsanity.com/paper/2507.21105/) (2025)
- [Position: The Real Barrier to LLM Agent Usability is Agentic ROI](https://mlsanity.com/paper/2505.17767/) (2025)
- [Skills on the Fly: Test-Time Adaptive Skill Synthesis for LLM Agents](https://mlsanity.com/paper/2605.16986/) (2026)
- [LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents](https://mlsanity.com/paper/2606.06087/) (2026)
- [AgentDNS: A Root Domain Naming System for LLM Agents](https://mlsanity.com/paper/2505.22368/) (2025)
- [Multi-Actor Generative Artificial Intelligence as a Game Engine](https://mlsanity.com/paper/2507.08892/) (2025)
- [LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities](https://mlsanity.com/paper/2601.09822/) (2026)
- [Quantifying Trust: Financial Risk Management for Trustworthy AI Agents](https://mlsanity.com/paper/2604.03976/) (2026)
- [Federation of Agents: A Semantics-Aware Communication Fabric for Large-Scale Agentic AI](https://mlsanity.com/paper/2509.20175/) (2025)
- [ACPs: Agent Collaboration Protocols for the Internet of Agents](https://mlsanity.com/paper/2505.13523/) (2025)
- [From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems](https://mlsanity.com/paper/2506.04565/) (2025)
- [ProtocolBench: Which LLM MultiAgent Protocol to Choose?](https://mlsanity.com/paper/2510.17149/) (2025)
- [UFO3: Weaving the Digital Agent Galaxy](https://mlsanity.com/paper/2511.11332/) (2025)

## Abstract (arXiv)

The rapid development of large language models (LLMs) has led to the widespread deployment of LLM agents across diverse industries, including customer service, content generation, data analysis, and even healthcare. However, as more LLM agents are deployed, a major issue has emerged: there is no standard way for these agents to communicate with external tools or data sources. This lack of standardized protocols makes it difficult for agents to work together or scale effectively, and it limits their ability to tackle complex, real-world tasks. A unified communication protocol for LLM agents could change this. It would allow agents and tools to interact more smoothly, encourage collaboration, and triggering the formation of collective intelligence. In this paper, we provide the first comprehensive analysis of existing agent protocols, proposing a systematic two-dimensional classification that differentiates context-oriented versus inter-agent protocols and general-purpose versus domain-specific protocols. Additionally, we conduct a comparative performance analysis of these protocols across key dimensions such as security, scalability, and latency. Finally, we explore the future landscape of agent protocols by identifying critical research directions and characteristics necessary for next-generation protocols. These characteristics include adaptability, privacy preservation, and group-based interaction, as well as trends toward layered architectures and collective intelligence infrastructures. We expect this work to serve as a practical reference for both researchers and engineers seeking to design, evaluate, or integrate robust communication infrastructures for intelligent agents.