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Project Idea: Codebase Genius#

Welcome to the Codebase Genius project. The goal of this project is to build an AI-powered, agentic system capable of autonomously generating comprehensive documentation for any given software repository from GitHub.

This project is built around a multi-agent architecture, where each intelligent agent is responsible for a distinct task such as file structure parsing, semantic understanding, diagram generation, and final documentation writing. These agents collaborate in a pipeline to analyze and document codebases effectively.

This document outlines the project's scope, agent-based architecture, and core functionalities to guide you in your development process.

Project Scope#

The primary objective is to create an agentic system that accepts a GitHub repository URL and produces quality markdown documentation. The system should be particularly effective for repositories written in Python and Jac.

A key feature will be the automatic generation of visual diagrams to represent the codebase's structure and flow.

Core Functionalities#

The final application should be able to perform the following tasks:

  • Clone a Repository: Fetch the source code from a given GitHub URL.
  • Analyze File Structure: Understand and map the complete directory and file layout.
  • Analyze Code Relationships: Parse the code to understand how different parts of the system interact (e.g., which functions call others, how classes are related). This is also known as building a Code Context Graph (CCG).
  • Generate Documentation: Create a final markdown document that includes descriptions of the code and visual aids.

High-Level Workflow#

The process can be broken down into a sequence of clear steps. The agent will first understand the "what" and "where" of the code and then dive deeper to understand the "how."

flowchart LR
    A[Clone the Repo] --> B[Get File and Folder Structure]
    B --> C[Retrieve and Analyze README.md]
    C --> D[Pass to LLM for High-Level Planning]
    D --> E[Iteratively Analyze Code Content]
    E --> F[Generate Final Documentation]

Workflow Steps#

  1. Clone the Repo Clone the target GitHub repository to access its files.

  2. Get File and Folder Structure Generate a map of the entire repository to understand the layout.

  3. Retrieve and Analyze README.md Use the README file for a high-level project summary.

  4. High-Level Planning An LLM uses this initial data to create a plan for which parts of the codebase to document first.

  5. Iteratively Analyze Code Content Parse source files to understand logic, structure, and relationships.

  6. Generate the Documentation Assemble a comprehensive markdown document with visual diagrams.

Proposed Architecture: A Multi-Agent System#

To accomplish this, a multi-agent architecture will be used. Think of it as a team of specialized AI agents managed by a supervisor.

Architecture

Components#

  • Supervisor Agent: Manages the workflow and orchestrates the agents.

  • Worker Agents: Each with a distinct role:

    • Repo Mapper: Analyzes structure and README.
    • Code Analyzer: Parses and understands source code.
    • DocGenie: Produces the documentation and diagrams.

Agent Responsibilities#

Code Genius Agent (Supervisor)#

  • Oversees the entire workflow.
  • Manages execution order and integration of all worker agents.
  • Ensures final output is cohesive and complete.

Repo Mapper#

Responsible for high-level repository mapping:

  • File Tree Generator: Builds a structured view of the file system, ignoring unnecessary files and folders (e.g., .git, node_modules, etc.).
  • Readme Summarizer: Extracts a concise summary from the README.md file to provide context for the documentation process.

Code Analyzer#

Performs in-depth code analysis:

  • Uses tools such as Tree-sitter for parsing.
  • Identifies functions, classes, and their relationships.
  • Builds the foundation for understanding code logic and interaction.

DocGenie#

Responsible for generating documentation:

  • Converts structured code insights into human-readable markdown.
  • Integrates visual diagrams to enhance clarity and comprehension.

Documentation Strategy#

To ensure that the documentation is both clear and complete, follow this three-phase strategy:

1. Initial Mapping#

  • Begin with a full map of the repository structure.
  • Helps understand the overall layout of the codebase.

2. Prioritized Exploration#

  • Focus on high-impact files (e.g., main.py, app.py, entry points).
  • Document these areas first for maximum value.

3. Backfill Coverage#

  • Complete documentation for remaining utility and support files.
  • Ensures completeness without sacrificing efficiency.

Inputs & Outputs#

Input: A GitHub repository URL (public repo for MVP) Output: Markdown (.md) files saved locally, containing comprehensive documentation of the repository

Final Notes#

  • The output should be quality markdown files.
  • It must be readable, logically structured, and visually aided with diagrams where applicable.
  • The system should be generalizable but optimized for Python and Jac repositories.
  • If you are able to support additional programming languages beyond Python and Jac, feel free to extend the system's capabilities to handle them as well.

Good Luck!#

Build smart, write clean, and may your agents generate world-class documentation! ✨