Conclusion

This project set out to address a fundamental challenge faced by developers: understanding unfamiliar codebases. By integrating large language models, graph databases, and agentic workflows, we created a Code Analysis Agent capable of transforming raw code into a structured, queryable, and visualizable knowledge base.

Throughout development, we emphasized both depth and flexibility. The system enables detailed exploration of functional relationships via micro-level agents, while also supporting strategic overviews through macro-level summaries and visualizations. By dynamically routing queries to the appropriate agent, the LangGraph-based architecture allows for nuanced, multi-faceted interactions with complex repositories.

Our technical choices, such as favoring Neo4j over Memgraph, GPT-4.1-mini over other LLM options, and custom GraphRAG pipelines over prebuilt solutions, were driven by a desire for precision, control, and future scalability. Extensive preprocessing ensured that both graph and vector representations of the codebase captured meaningful, semantically rich information, enabling a smooth user experience during querying.

While the system achieved its primary goals, evaluation highlighted several areas for future improvement, including efficiency bottlenecks, smarter entity resolution, and more cohesive micro/macro agent coordination. Addressing these challenges could further enhance the system’s usability and scalability, making it suitable for larger and more diverse real-world codebases.

Ultimately, the Code Analysis Agent demonstrates how modern AI tools can significantly reduce the cognitive burden of code comprehension. By turning static source code into a dynamic, interactive knowledge system, this project lays the groundwork for more intelligent, accessible, and intuitive approaches to understanding and navigating software architecture.