Abstract
This paper presents a novel framework for automated code generation and debugging, designed to improve accuracy, efficiency, and scalability in software development. The proposed system integrates three core components—LangGraph, GLM-4-Flash, and ChromaDB—within a four-step iterative workflow to deliver robust performance and seamless functionality. LangGraph serves as a graph-based library for orchestrating tasks, providing precise control and execution while maintaining a unified state object for dynamic updates and consistency. It supports multi-agent, hierarchical, and sequential processes, making it highly adaptable to complex software engineering workflows. GLM-4-Flash, a large language model, leverages its advanced capabilities in natural language understanding, contextual reasoning, and multilingual support to generate accurate code snippets based on user prompts. ChromaDB acts as a vector database for semantic search and contextual memory storage, enabling the identification of patterns and the generation of context-aware bug fixes based on historical data. The system operates through a structured four-step process: (1) Code Generation, which translates natural language descriptions into executable code; (2) Code Execution, which validates the code by identifying runtime errors and inconsistencies; (3) Code Repair, which iteratively refines buggy code using ChromaDB’s memory capabilities and LangGraph’s state tracking; and (4) Code Update, which ensures the code meets functional and performance requirements through iterative modifications. By combining advanced task orchestration, semantic reasoning, and memory-driven insights, the system achieves precise and iterative debugging, significantly enhancing software development workflows. This work represents a substantial advancement in automated software engineering, addressing critical challenges in code reliability and runtime error resolution.