Introduction

Mindify Lab’s CURA (Contextual Understanding & Reasoning Agent) is more than just a language model—it’s a hybrid system designed to deeply understand your codebase, maintain context across sessions, and provide actionable insights.

CURA’s key strengths:

Contextual Awareness: It tracks file structure, dependencies, and your project’s state.
Reasoning Engine: It applies symbolic reasoning to validate assumptions and catch edge cases.
Human-like Explanations: It generates responses that bridge the gap between code and concept.

Core Architecture

1. Contextual Encoder

What it does: Ingests your project’s files, open editors, and recent commits.
How it works:
1. Tokenizes file contents with syntax-awareness.
2. Embeds repository metadata (e.g., Git history, issue tracker data).
3. Produces a unified context vector.

2. Reasoning Module

What it does: Applies multi-step logical reasoning to the context vector.
How it works:
- Uses a symbolic verifier to check for common pitfalls (e.g., memory leaks, race conditions).
- Runs a constraint solver to propose minimal, safe changes.
- Integrates domain-specific heuristics (e.g., React hook cleanup patterns).

3. Output Synthesizer

What it does: Crafts human-readable explanations and code suggestions.
How it works:
- Leverages a fine-tuned GPT backbone for natural language generation.
- Merges symbolic recommendations back into code snippets.
- Formats responses with inline code blocks and references.

Example Workflow

import { CURA } from "@mindify-ai/cura";

(async () => {
  const analysis = await CURA.analyze({
    prompt: "Why am I seeing a memory leak when switching React routes?",
    context: {
      files: ["App.jsx", "Routes.jsx"],
      hooks: ["useEffect", "useLayoutEffect"],
    },
  });

  console.log(analysis.insights);
})();