Nokode AgentOS – Restructured Architecture

This repository contains a restructured version of the original Nokode AgentOS codebase. The goal of this restructuring is to blend the best architectural ideas from two cutting‑edge AI coding platforms—Lovable.dev and Emergent—while preserving the original intent of each module. The original code consisted mostly of placeholder files with comments; those placeholders have been migrated into a more modular and extensible project layout that mirrors how mature AI‑driven no‑code platforms organise their code.

High‑Level Overview

Multi‑Agent System (Emergent‑inspired)

Emergent’s “vibe‑coding” approach uses specialised agents that work together to build, test and deploy full‑stack applications【410284737465147†L69-L75】. In this restructure:

• agents/ contains separate modules for each type of AI agent—one for generating the front‑end, one for building the back‑end, one for designing the database, one for quality assurance, and one for deployment. These agents orchestrate tasks internally and can be extended to call large language models or other services.

Developer‑Friendly Code Export (Lovable‑inspired)

Lovable.dev emphasises generating real, editable code that is exported to GitHub【955008867522803†L90-L106】. To support that mindset:

• frontend/ contains React/Tailwind UI components, pages and assets that can be edited by developers.
• backend/ provides API routes, services and integration points that can be customised or extended with Python/Flask (or another framework of your choice).
• emails/ holds HTML templates for transactional emails.
• public/ serves static assets such as landing pages.
• configs/ keeps deployment configuration (e.g. vercel.json).

The structure encourages separation of concerns so that generated code isn’t a monolith; instead, developers can modify a specific area without breaking the entire system.

Directory Layout

nokode_restructured/
├── README.md               # This file – explains the architecture
├── agents/                 # AI agent modules (Emergent style)
│   ├── blueprint_generator.py   # Converts blueprints into code
│   ├── frontend_agent.py        # Plans and builds the UI
│   ├── backend_agent.py         # Generates API endpoints and business logic
│   ├── db_agent.py              # Designs data models and migrations
│   ├── qa_agent.py              # Runs tests and fixes bugs
│   └── deployment_agent.py      # Handles CI/CD and deployment tasks
├── backend/                # Back‑end API and services
│   ├── routes/
│   │   ├── admin/
│   │   │   └── analytics.py    # OpenGraph + webhook analytics stub
│   │   └── agents/
│   │       └── list.py         # API to return list of agents with status
│   ├── services/
│   │   └── llm_selector.py     # Chooses between GPT/Claude/LLaMA based on logic
│   └── __init__.py
├── configs/
│   └── vercel.json         # Deployment configuration
├── emails/
│   └── templates/
│       ├── usage_warning.html   # <p>You are nearing usage limit.</p>
│       └── welcome_email.html   # <h1>Welcome to Nokode</h1>
├── frontend/
│   ├── pages/
│   │   └── admin/
│   │       └── AgentDashboard.jsx  # Agent dashboard UI – status of Claude, GPT, LLaMA
│   ├── components/
│   │   └── MonacoPanel.jsx         # Monaco editor with preview/fork/save logic
│   └── assets/
├── public/
│   ├── landing.html           # Stub landing page
│   └── assets/
└── tests/
    └── README.md             # Placeholder for future test descriptions

Next Steps

1. Implement AI agents: Flesh out the modules in the agents/ directory so that each can call large language models, generate or modify code, and communicate with the others. For example, the frontend_agent could convert a blueprint into Tailwind/React code and save it into the frontend/ folder.

2. Define API routes: Complete the stub files in backend/routes/ to expose REST/GraphQL endpoints. Connect them to services in backend/services/ and use the llm_selector to choose the appropriate model for each task.

3. Build the UI: Expand the components in frontend/ to create an interactive dashboard, an editor, and other pages. Use frameworks like Next.js or Remix for server‑side rendering if desired.

4. Add testing and CI: Introduce automated tests in tests/ and set up continuous integration to run them. This echoes Emergent’s philosophy of continuous testing【410284737465147†L86-L102】.

5. Document everything: Extend this README and add additional documentation in docs/ to help developers understand how to customise and extend the platform.

By following this structure, you get the rapid scaffolding and editable code promised by Lovable.dev【955008867522803†L90-L106】, combined with the modular, multi‑agent workflow championed by Emergent【410284737465147†L69-L75】. It’s a flexible foundation for building AI‑powered no‑code tools.