{"ID":2842681,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09087","arxiv_id":"2511.09087","title":"Tele-LLM-Hub: Building Context-Aware Multi-Agent LLM Systems for Telecom Networks","abstract":"This paper introduces Tele-LLM-Hub, a user friendly low-code solution for rapid prototyping and deployment of context aware multi-agent (MA) Large Language Model (LLM) systems tailored for 5G and beyond. As telecom wireless networks become increasingly complex, intelligent LLM applications must share a domainspecific understanding of network state. We propose TeleMCP, the Telecom Model Context Protocol, to enable structured and context-rich communication between agents in telecom environments. Tele-LLM-Hub actualizes TeleMCP through a low-code interface that supports agent creation, workflow composition, and interaction with software stacks such as srsRAN. Key components include a direct chat interface, a repository of pre-built systems, an Agent Maker leveraging finetuning with our RANSTRUCT framework, and an MA-Maker for composing MA workflows. The goal of Tele-LLM-Hub is to democratize the design of contextaware MA systems and accelerate innovation in next-generation wireless networks.","short_abstract":"This paper introduces Tele-LLM-Hub, a user friendly low-code solution for rapid prototyping and deployment of context aware multi-agent (MA) Large Language Model (LLM) systems tailored for 5G and beyond. As telecom wireless networks become increasingly complex, intelligent LLM applications must share a domainspecific u...","url_abs":"https://arxiv.org/abs/2511.09087","url_pdf":"https://arxiv.org/pdf/2511.09087v2","authors":"[\"Pranshav Gajjar\",\"Cong Shen\",\"Vijay K Shah\"]","published":"2025-11-12T08:01:15Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}