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This paper introduces a multi-agent framework leveraging LangChain and LLMs to automate the generation, debugging, execution, and analysis of complex 5G network simulations within the ns-3 network simulator. The framework employs specialized agents for simulation generation, test design, execution, and result interpretation, using chain-of-thought reasoning and retrieval-augmented generation to translate natural language specifications into ns-3 scripts. A case study using the ns-3 5G-LENA module demonstrates the effectiveness of the approach, achieving convergence in code generation with quantifiable metrics for error rate, response time, and human evaluation score.
Automating 5G network simulation with LLMs slashes development time, converging on executable code in under two iterations.
The move toward open Sixth-Generation (6G) networks necessitates a novel approach to full-stack simulation environments for evaluating complex technology developments before prototyping and real-world implementation. This paper introduces an innovative approach11 A lightweight, mock version of the code is available on GitHub at https://github.com/frezazadeh/LangChain-RAG-Technology that combines a multi-agent framework with the Network Simulator 3 (ns-3) to automate and optimize the generation, debugging, execution, and analysis of complex Fifth-Generation (5G) network scenarios. Our framework orchestrates a suite of specialized agents—namely, the Simulation Generation Agent, Test Designer Agent, Test Executor Agent, and Result Interpretation Agent—using advanced LangChain coordination. The Simulation Generation Agent employs a structured chain-of-thought (CoT) reasoning process, leveraging large language models (LLMs) and retrieval-augmented generation (RAG) to translate natural language simulation specifications into precise ns-3 scripts. Concurrently, the Test Designer Agent generates comprehensive automated test suites by integrating knowledge retrieval techniques with dynamic test case synthesis. The Test Executor Agent dynamically deploys and runs simulations, managing dependencies and parsing detailed performance metrics. At the same time, the Result Interpretation Agent utilizes LLM-driven analysis to extract actionable insights from the simulation outputs. By integrating external resources such as library documentation and ns-3 testing frameworks, our experimental approach can enhance simulation accuracy and adaptability, reducing reliance on extensive programming expertise. A detailed case study using the ns-3 5G-LENA module validates the effectiveness of the proposed approach. The code generation process converges in an average of 1.8 iterations, has a syntax error rate of 17.0%, a mean response time of 7.3 seconds, and receives a human evaluation score of 7.5.
