The Code Fable

Visualize your codebase as an interactive story. AI analyzes architecture, identifies hotspots, and reveals hidden patterns that help in turning complex code into clear insights,

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Inspiration

Understanding a large codebase is one of the hardest parts of contributing to open source. New contributors often feel lost jumping between files, imports, and unfamiliar patterns.
We wanted to build something that turns a repository into a story—so developers can understand how and why the code works, not just what it contains.

That idea became The Code Fable.

What it does

The Code Fable analyzes a public GitHub repository and transforms it into an interactive, narrative-driven explanation.

  • Parses the repository structure and source files
  • Builds a dependency graph of files and modules
  • Extracts key functions, classes, and responsibilities
  • Generates a human-readable story explaining how components interact
  • Visualizes relationships for intuitive exploration

How we built it

  • Analyzer: Deterministic static analysis to extract imports, symbols, and summaries
  • Graph Builder: Directed dependency graph (files/modules as nodes, imports/usages as edges)
  • Store & Cache: Persists analysis results for performance and scalability
  • LLM Narrative Layer: Synthesizes explanations strictly from verified analysis
  • Frontend: Interactive UI to explore graphs and narratives

Clear separation of concerns keeps the system predictable and scalable.

Challenges we ran into

  • Handling large repositories efficiently
  • Keeping explanations grounded in real analysis (not hallucinations)
  • Managing complex dependency graphs
  • Designing prompts that produce useful, non-generic explanations

Accomplishments that we're proud of

  • A tool that genuinely helps developers onboard faster
  • Strong separation between analysis and explanation
  • Scalable architecture suitable for large codebases
  • Meaningful visualizations instead of shallow metrics
  • A complete working product within hackathon constraints

What we learned

  • Developer tools need clarity more than features
  • Static analysis + LLMs work best with strict boundaries
  • Visualization should answer real questions
  • Prompt engineering is system design, not copywriting

What's next for The Code Fable

  • Support for private repositories
  • Deeper semantic and architectural analysis
  • Time-based evolution views for long-lived projects
  • Exportable onboarding documentation
  • IDE integrations for in-editor narratives

PS:

I planned to deploy the full stack (backend API, Redis, and background workers), but most free-tier platforms don’t support persistent Redis-backed workers or multi-service setups. Since Redis is central to caching and task queues in this project, a complete free-tier deployment wasn’t feasible without cutting core functionality.

That said, the system is fully containerized and works end-to-end locally. I’ve also documented a production-ready deployment setup (Docker + managed Redis + workers) that can be used on platforms or self-hosted environments that support it.

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