Codebase Intelligence Agent

Problem & Solution Statement Understanding an unfamiliar software project is one of the biggest challenges for developers joining an existing team. Developers often need to spend hours reading source code, searching through files, understanding dependencies, finding APIs and entry points, and figuring out how different components interact before they can contribute effectively. Traditional documentation is often incomplete, outdated, or disconnected from the actual implementation. OnboardAI is an AI-powered Developer Onboarding and Codebase Intelligence Agent designed to reduce this learning curve. A developer provides a public GitHub repository URL, and the system analyzes the repository without executing its code. It identifies files, programming languages, modules, symbols, relationships, dependencies, API routes, configuration information, and other structural information from the codebase. The analyzed repository is transformed into structured knowledge and semantically meaningful chunks. Embeddings are generated using a Transformer-based embedding model and stored in PostgreSQL with pgvector. This knowledge forms the foundation of a Retrieval-Augmented Generation (RAG) pipeline. After analysis, the system can generate a comprehensive onboarding guide explaining the project's purpose, architecture, structure, important components, entry points, APIs, dependencies, setup information, and relationships between different parts of the codebase. Developers can also interact with the project through an AI-powered chatbot. Instead of sending the entire repository to the language model, the system retrieves relevant repository knowledge and provides that context to the LLM before generating an answer. This helps keep responses grounded in the actual codebase. The target users are software developers, engineering teams, technical leads, and developers joining unfamiliar projects. A developer interacts with the system through a web interface by submitting a repository and then asking natural-language questions such as where a feature is implemented, how a particular flow works, or which component acts as an entry point. The project's key innovation is combining automated codebase analysis, structured repository knowledge, semantic retrieval, RAG, and AI-generated onboarding documentation into a single developer-focused system. It transforms a repository from a collection of files into an interactive knowledge source that developers can explore conversationally. Security is also a core consideration: repository code is analyzed statically and is not executed, credentials are kept in environment variables, and repository/resource restrictions are applied during ingestion.