Agamify

Universal Codebase Translator + Function Visualizer

Abstract

Agamify is an intelligent system designed to translate, refactor, and visualize source code across any kind of codebase—frontend, backend, scripting, system-level, mobile, and more. It automates cross-language and cross-framework migration while preserving logic, architecture, and functionality, enabling seamless portability, modernization, and collaborative understanding of software systems.

At its core, Agamify provides:

Cross-Migration: Move your existing projects—regardless of language or framework—into other supported environments without manual rewriting.

Rapid Prototyping: Build core logic once and instantly generate equivalent implementations in multiple ecosystems for fast iteration and testing.

Developer Education: Understand how different languages and paradigms handle the same logic through side-by-side comparisons and visual representations.

Framework & Language Benchmarking: Compare performance, structure, and readability across implementations to make informed technology choices.

Modernize Legacy Code: Refactor outdated, monolithic, or unstructured codebases into clean, maintainable, modern architectures.

Team Collaboration: Visualize and standardize component and module structures across teams, enabling consistent architecture and shared library migration.

To support this, Agamify features a powerful Function Visualizer that performs static analysis on both original and translated code to generate interactive views of function call graphs, data flow, and component/module hierarchies. This visual layer enhances understanding, debugging, and confidence in translated outputs.

By combining large language model (LLM)-powered translation with deep visual insight, Agamify enables a new level of productivity and clarity in working with complex or legacy codebases—supporting use cases from enterprise modernization and research to teaching, rapid development, and platform migration.

Abstract Architecture

Supported Frameworks

Feature	Description
CROSS-MIGRATION	Migrate any codebase—web, backend, scripts, mobile, etc.—to other supported stacks without manual rewriting.
RAPID PROTOTYPING	Build logic once, then instantly generate equivalent implementations across multiple environments for fast iteration.
DEVELOPER EDUCATION	Explore how different languages/frameworks handle the same logic via side-by-side comparisons and visual mapping.
FRAMEWORK & LANGUAGE BENCHMARKING	Compare structure, readability, and performance across different implementations—useful when selecting tech stacks.
MODERNIZE LEGACY CODE	Refactor outdated codebases into clean, maintainable, and modular modern structures.
TEAM COLLABORATION	Ensure architectural consistency across teams by visualizing module structures and migrating shared libraries.

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🎯 Whether you're a solo developer, part of a team, or modernizing enterprise applications—these features streamline migration, learning, and collaboration across the entire software lifecycle.

Workflow

Cross-Framework Migration Pipeline (via DOM + RAG + Transpilation)

1. Language Identification (Deterministic Check)

Statistically or syntactically determine the source programming language and framework of the input codebase with high confidence using AST analysis and rule-based heuristics.

2. Dependency-Aware Language Rebuild

Construct the logical representation of the app by analyzing dependencies (components, functions, styles, etc.) to create an optimal import graph. This ensures a modular and minimal rebuild structure.

3. Transpile to Vanilla JavaScript/HTML (Intermediate Form)

Convert the framework-specific code (e.g., React, Vue) to a vanilla JavaScript and DOM-equivalent form, serving as a common language-neutral representation.

4. RAG-Based Component Mapping

Use a Retrieval-Augmented Generation (RAG) system to map semantic structures (like lifecycle hooks, props, bindings) from the original framework to equivalent concepts in target frameworks. This ensures high-fidelity transformations.

5. DOM Crawler Construction

Build a DOM crawler to traverse and extract leaf-to-root relationships of rendered components, collecting both structure and behavioral metadata from the vanilla intermediate.

6. Functional DOM Visualizer

Render an interactive visual tree of the application's DOM and component structure, enriched with metadata such as event handlers, state bindings, and data flows for each node.

7. Leaf-to-Root Conversion Pipeline

Begin translation from the leaf nodes (atomic components/functions) and recursively build toward the root. This approach maintains dependency integrity and ensures correct resolution order.

8. Modular Code Generation (Target Framework)

Generate modular, idiomatic code for the target framework by assembling translated components based on the dependency tree, preserving the structure and functionality of the original application.

Outcome

A fully migrated, modular, and maintainable codebase in one or more target web frameworks (e.g., from React → Vue, Angular, Svelte, etc.), with visual verification and explainability built into the process.

RAG Pipeline for Framework-to-Framework Code Migration

Purpose

To enable accurate and explainable translation of frontend application code from one framework to others using a combination of retrieval (knowledge base of framework patterns, idioms, lifecycle hooks, etc.) and generation (LLM-based transformation).

1. Input Processing

• Input: Source code (e.g., React project)
• Goal: Extract all relevant code artifacts (components, state logic, lifecycle methods, routing config)
• Tooling: AST parsers (e.g., Babel, ts-morph), dependency resolver, file tree scanner

2. Document Chunking (Knowledge Base Construction)

Sources:

• Documentation from target frameworks (Vue, Angular, Svelte, etc.)
• Real-world code examples (component structures, routing, state management)
• Migration guides and lifecycle equivalency mappings

Chunks:

• Lifecycle equivalence mappings (e.g., componentDidMount → onMounted)
• Syntax patterns (JSX → Vue templates, directives, bindings)
• Behavior mappings (e.g., useState vs reactive data)

Format and Storage:

• Format: Structured JSON or Markdown embeddings
• Storage: FAISS / Chroma / Weaviate embedding store

3. Query Generation (From Code)

Generate queries such as:

• "How do I migrate useEffect to Angular?"
• "What is the equivalent of React props in Svelte?"
• "How is component composition done in Vue?"

Each component, hook, and function can create one or more semantic queries using natural language + code prompt hybrid queries.

4. Retrieval (Context Injection)

• Retrieve top-k relevant chunks from the embedding store using vector search (e.g., OpenAI embeddings or custom)
• Inject retrieved documents into prompt for the LLM
• Apply ranking and deduplication (optional)

5. LLM-Based Generation

Use OpenAI GPT-4.5, Claude, or Mixtral to:

• Translate the structure of components
• Convert syntax (JSX → SFC, template → TS class, etc.)
• Preserve logic, data flow, and comments

Prompt includes:

• Retrieved context (docs, patterns)
• Source code snippet
• Target framework and constraints

Example prompt:

Given the following React component and context about Vue.js, convert this to a modular Vue 3 SFC. Preserve logic, props, state, and effects. Output only code.

6. Validation & Feedback

Use static analysis tools to validate:

• Correctness of generated code
• Presence of lifecycle/state mappings
• Modular structure adherence
• Optionally, round-trip validate by re-transpiling to intermediate form

7. Visual DOM + Function Mapping

Use visual graph of DOM and component tree as:

• Verification aid
• Traversal strategy for leaf-to-root generation
• Map each DOM node and its logic to its generated equivalent in the new framework

8. Output

• Modular codebase in target framework
• Side-by-side diff or visualization between source and target
• Optional zip/export or Git integration

Github Auth

Push Branch

Database Schema Implementation

MongoDB + Prisma Setup

We have successfully implemented a production-ready database schema using Prisma ORM with MongoDB Cloud. Here's what has been set up:

Database Models

• User: Core user information with GitHub integration
• Repository: Git repositories with metadata
• Branch: Git branches within repositories
• Language: Programming languages/frameworks
• RepoUser: Junction table for user-repository relationships

Folder Structure

Frontend/
├── lib/
│   └── database/
│       ├── prisma.ts          # Database connection
│       ├── user.service.ts     # User operations
│       ├── repository.service.ts # Repository operations
│       ├── branch.service.ts   # Branch operations
│       ├── language.service.ts # Language operations
│       ├── utils.ts           # Database utilities
│       └── index.ts           # Exports
├── types/
│   └── database.ts            # TypeScript types
├── pages/api/
│   ├── database/
│   │   └── test.ts           # Database test endpoint
│   ├── users/
│   │   ├── index.ts          # User CRUD
│   │   └── [id].ts           # User by ID
│   └── repositories/
│       └── index.ts          # Repository CRUD
├── prisma/
│   └── schema.prisma         # Database schema
└── scripts/
    └── seed.ts               # Database seeding

Connection Details

• Provider: MongoDB Atlas Cloud
• Database: agamify
• Connection: Secured with credentials
• Features:
  • Type-safe queries with Prisma
  • Production-ready error handling
  • Transaction support
  • Connection pooling

API Endpoints

• GET /api/database/test - Test database connection
• POST /api/users - Create user
• GET /api/users?email=x - Find user by email
• POST /api/repositories - Create repository
• GET /api/repositories?userId=x - Get user repositories

Available Scripts

npm run db:generate  # Generate Prisma client
npm run db:push      # Push schema to MongoDB
npm run db:studio    # Open Prisma Studio
npm run db:seed      # Seed database with sample data
npm run db:reset     # Reset and reseed database

Schema

![image]()

Tech Stack

Technology	Role
Next.js	Frontend + backend fullstack React framework (API routes, SSR, etc.)
Prisma	ORM to interact with databases (PostgreSQL, MongoDB)
MongoDB (Free)	NoSQL database for early-stage data storage
Docker	Containerization for consistent environments
PostgreSQL	Relational DB used when scaling beyond MongoDB's capabilities
Redis	In-memory cache for performance (sessions, rate limits, etc.)
LangChain	Framework to orchestrate LLM calls, memory, tools in RAG pipeline
OpenAI Embeddings	Converts text to vectors for semantic search or RAG
FAISS	Vector store to index and search embeddings efficiently
Stripe	Payment gateway for managing subscriptions and transactions
Azure	Deployment platform for hosting fullstack apps + databases