为5G网络打造可感知上下文的多智能体大模型系统，降低开发门槛。
Tele-LLM-Hub: Building Context-Aware Multi-Agent LLM Systems for Telecom Networks
- 设计电信专用上下文协议，实现智能体间结构化通信。
- 提供低代码界面，支持快速构建和部署多智能体系统。
- 适合通信领域研究者与工程师快速试验大模型应用。
本文提出Tele-LLM-Hub，一个面向5G及未来无线网络的用户友好型低代码解决方案，用于快速原型设计与部署上下文感知的多智能体（MA）大语言模型（LLM）系统。随着电信无线网络日益复杂，智能LLM应用需共享对网络状态的领域特定理解。为此，我们提出电信模型上下文协议（TeleMCP），以实现电信环境中智能体间的结构化、上下文丰富的通信。Tele-LLM-Hub通过低代码界面实现TeleMCP，支持智能体创建、工作流组合以及与srsRAN等软件栈交互。核心组件包括直接聊天界面、预构建系统仓库、基于RANSTRUCT框架微调的Agent Maker，以及用于组合多智能体工作流的MA-Maker。Tele-LLM-Hub旨在普及上下文感知多智能体系统的设计，加速下一代无线网络的创新。
原文摘要 · Abstract (English)
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.
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