mlsanityData to 14 Oct 2026

AgentDNS: A Root Domain Naming System for LLM Agents

arXiv 2505.22368 ↗First posted 28 May 2025cs.AIcited by 8

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

The rapid evolution of Large Language Model (LLM) agents has highlighted critical challenges in cross-vendor service discovery, interoperability, and communication. Existing protocols like model context protocol and agent-to-agent protocol have made significant strides in standardizing interoperability between agents and tools, as well as communication among multi-agents. However, there remains a lack of standardized protocols and solutions for service discovery across different agent and tool vendors. In this paper, we propose AgentDNS, a root domain naming and service discovery system designed to enable LLM agents to autonomously discover, resolve, and securely invoke third-party agent and tool services across organizational and technological boundaries. Inspired by the principles of the traditional DNS, AgentDNS introduces a structured mechanism for service registration, semantic service discovery, secure invocation, and unified billing. We detail the architecture, core functionalities, and use cases of AgentDNS, demonstrating its potential to streamline multi-agent collaboration in real-world scenarios. The source code will be published on https://github.com/agentdns.

Key facts. Cited by 8 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 14 of its 30 references that are in the map. First posted on arXiv 28 May 2025.

Builds on

14 of 30 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025A Survey of AI Agent Protocols23 Apr
  2. 2025Large Language Model Agent: A Survey on Methodology, Applications and Challenges27 Mar
  3. 2024AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation1 Aug
  4. 2024Improving Multi-Agent Debate with Sparse Communication Topology17 Jun
  5. 2024Understanding the planning of LLM agents: A survey5 Feb

Other references · 9

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  2. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  3. 2024WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learningcited by 256
  4. 2024MindSearch: Mimicking Human Minds Elicits Deep AI Searchercited by 66
  5. 2024Large Language Model Agent in Financial Trading: A Surveycited by 60
  6. 2025LLM Agents for Education: Advances and Applicationscited by 59
  7. 2024AGILE: A Novel Reinforcement Learning Framework of LLM Agentscited by 45
  8. 2024A Scalable Communication Protocol for Networks of Large Language Modelscited by 45
  9. 2025Encrypted Large Model Inference: The Equivariant Encryption Paradigmcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Indexing the Unreadable: LLM-Native Recursive Construction and Search of Service Taxonomies
  2. 2026A Technical Taxonomy of LLM Agent Communication Protocols
  3. 2025Agent Name Service (ANS): A Universal Directory for Secure AI Agent Discovery and Interoperability
  4. 2026LLM-Enabled Multi-Agent Systems: Empirical Evaluation and Insights into Emerging Design Patterns & Paradigms
  5. 2026LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities
  6. 2025Seven Security Challenges in Cross-domain Multi-agent LLM Systems
  7. 2026LLM Agents Factory: Retrieval of Domain-Specific LLM Agents
  8. 2025AgentHub: A Registry for Discoverable, Verifiable, and Reproducible AI Agents