English
Nexus: A Lightweight and Scalable Multi-Agent Framework for Complex Tasks Automation
Humza Sami¹, Mubashir ul Islam¹, Samy Charas¹, Asav Gandhi¹, Pierre-Emmanuel Gaillardon¹˒², and Valerio Tenace¹
¹PrimisAI, Los Gatos, CA, USA
²University of Utah, Salt Lake City, UT, USA
Recent advancements in Large Language Models (LLMs) have substantially evolved Multi-Agent Systems (MASs) capabilities, enabling systems that not only automate tasks but also leverage near-human reasoning capabilities. To achieve this, LLM-based MASs need to be built around two critical principles: (i) a robust architecture that fully exploits LLM potential for specific tasks -- or related task sets -- and (ii) an effective methodology for equipping LLMs with the necessary capabilities to perform tasks and manage information efficiently. It goes without saying that a priori architectural designs can limit the scalability and domain adaptability of a given MAS.
To address these challenges, in this paper we introduce Nexus: a lightweight Python framework designed to easily build and manage LLM-based MASs. Nexus introduces the following innovations: (i) a flexible multi-supervisor hierarchy, (ii) a simplified workflow design, and (iii) easy installation and open-source flexibility: Nexus can be installed via pip and is distributed under a permissive open-source license, allowing users to freely modify and extend its capabilities.
Experimental results demonstrate that architectures built with Nexus exhibit state-of-the-art performance across diverse domains. In coding tasks, Nexus-driven MASs achieve a 99% pass rate on HumanEval and a flawless 100% on VerilogEval-Human, outperforming cutting-edge reasoning language models such as o3-mini and DeepSeek-R1. Moreover, these architectures display robust proficiency in complex reasoning and mathematical problem solving, achieving correct solutions for all randomly selected problems from the MATH dataset. In the realm of multi-objective optimization, Nexus-based architectures successfully address challenging timing closure tasks on designs from the VTR benchmark suite, while guaranteeing, on average, a power saving of nearly 30%.
|
E-mail This Article | 
|
|
Printer-Friendly Page |
|
||||||
Related Articles
- Right Python Framework Selection for Automation Testing
- System on Modules (SOM) and its end-to-end verification using Test Automation framework
- A NoC-based Communication Framework For Seamless IP Integration in Complex Systems
- A NoC-Based Communication Framework for Seamless IP Integration in Complex Systems
- Fully Automating Chip Design
New Articles
- Nexus: A Lightweight and Scalable Multi-Agent Framework for Complex Tasks Automation
- How the Ability to Manage Register Specifications Helps You Create More Competitive Products
- EAVS - Electra IC Advanced Verification Suite for RISC-V Cores
- Why RISC-V is a viable option for safety-critical applications
- Dimensioning in 3D space: Object Volumetric Measurement by Leveraging Depth Camera-based Reconstruction on NVIDIA Edge devices
Most Popular
- System Verilog Assertions Simplified
- System Verilog Macro: A Powerful Feature for Design Verification Projects
- PCIe error logging and handling on a typical SoC
- Enhancing VLSI Design Efficiency: Tackling Congestion and Shorts with Practical Approaches and PnR Tool (ICC2)
- Synthesis Methodology & Netlist Qualification