From Code to Execution: Multi-Agent GraphRAG for Automated Artifact Generation

Abstract

Modern data and Machine Learning (ML) pipelines are developed as code repositories but must be manually transformed into deployable artifacts such as Docker images and Kubernetes workflows, a process prone to dependency and configuration errors. While Large Language Models (LLMs) can assist with code understanding, they often fail on multifile repositories due to missing structural context and incomplete dependency resolution. We propose a multi-agent graph-based Retrieval-Augmented Generation (GraphRAG) framework that automates the generation of deployment artifacts from code repositories. The framework constructs an Abstract Syntax Tree (AST)-grounded knowledge graph, performs structure-aware retrieval, and coordinates specialized agents to synthesize containerization and workflow specifications. The system further improves reliability through iterative self-healing driven by execution logs. Evaluated on 17 GitHub repositories spanning ETL, ML training, and LLM-based evaluation pipelines, our approach achieves 65% end-to-end execution success on Kubernetes, outperforming a vector-only RAG baseline (24%) and a single-agent GraphRAG variant (47%). These results indicate that combining graph-centric retrieval with agent decomposition improves execution reliability.