# Tele-LLM-Hub: Building Context-Aware Multi-Agent LLM Systems for Telecom Networks

arXiv: https://arxiv.org/abs/2511.09087 · HTML: https://mlsanity.com/paper/2511.09087/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- First posted on arXiv 12 Nov 2025.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Built on by (most cited first, 2 of 2)

- [LLM-Powered Agentic AI for 5G/6G Networks: A Tutorial and Survey on Architectures, Protocols, and Standardization](https://mlsanity.com/paper/2607.16066/) (2026)
- [CRAFT: Fine-Tuning Pre-hoc Explainability in AI-native 6G RAN](https://mlsanity.com/paper/2609.00590/) (2026)

## Abstract (arXiv)

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.