How VENORE Actually Works

VENORE was never meant to be a magical system that understands your architecture out of nowhere. It is a tool built on simple and transparent ideas, relying only on information that already exists in your project. It does not guess. It does not assume. It reflects reality.

Here is how it really works under the hood.

1. Context files as the entry point to understanding

The first principle behind VENORE is that the code should be able to explain its own intention. This is why the foundation of the system is a set of small .context.md files placed in important folders.

These files contain minimal documentation that combines purpose, metadata and status for each module. Their structure is simple but powerful.

# module-name

> Auto generated by Veronica Context Agent

## Description
What this module does and why it exists.

## Status
`stable`

## Layers
| Layer   | Status    |
|---------|-----------|
| context | complete  |
| docs    | complete  |

## Tags
`api` `auth`

The files are generated automatically, but they are meant to be reviewed and improved. They live inside the repository and evolve together with the code. Their purpose is to provide the semantic seed that VENORE uses to build a coherent global view of the project.

2. Static analysis that observes without interfering

VENORE does not run your application. It does not inject itself inside the runtime. Instead, it performs static analysis to detect project structure, file relationships and the progress of each module during the initial scan.

This information is stored in a simple file called .veronica/analysis-progress.json.

{
  "status": "completed",
  "totalFolders": 5,
  "analyzedFolders": 5,
  "contextsCreated": 5
}

Static analysis in VENORE is intentionally lightweight. It does not attempt to replace more advanced tools. Its goal is to extract just enough structure to build a meaningful visual representation. The priority is fidelity, not complexity.

3. A lightweight RAG system for semantic understanding

The next layer is a small Retrieval Augmented Generation system that allows VENORE to understand the project without fully parsing the source code.

It uses four main components:

• chunks.json to store text segments extracted from context files
• embeddings.bin to store vector representations
• index.json to map chunks to their vectors
• snapshot.md to provide a unified high level summary of the project

An example snapshot looks like this:

# project-name

## Overview
A short description of what this project does.

## Architecture
Main modules
Key components
Utilities

## Tech Stack
Astro, React, TypeScript

This structure makes it possible to answer questions about the system by relying on documented and verified context instead of blind guesswork.

4. AI that explains instead of generating code

The role of AI inside VENORE is narrow on purpose. It reads, analyzes and explains. It does not write code. It does not modify anything.

When you ask a question, VENORE retrieves the most relevant chunks through the vector index, organizes the information and generates an explanation based strictly on what exists in the repository. This avoids hallucinations and ensures that all answers remain grounded in real context.

What comes next

VENORE is still evolving. The plan is to expand its capabilities while keeping the core simple and transparent. Some of the areas being explored include:

• Better dependency detection
• Real time canvas updates
• Native multi project support
• Smarter context generation assisted by AI
• Stronger integration between static analysis and semantic layers

The goal is not to turn VENORE into a massive tool, but to reinforce its purpose: understanding the real architecture of a project without unnecessary complexity.

Final thought

VENORE began as an experiment to answer a simple question: How can I see my project as it truly is?

If it succeeds in making complex systems easier to understand, even a little, it will have achieved its technical purpose.

Edinson