The Architectural Oversight Layer
for AI-Native Development
You focus on design. AI writes the code like a real software engineer.
Living architecture diagrams keep AI agents aligned — dramatically reducing token usage
Proven Performance
Early benchmarks show dramatic improvements when AI agents use kratai's architecture context
Fewer Tokens
Reduction in output tokens generated
Less Input
Reduction in total input tokens
Lower Costs
Reduction in billing units
Faster
Faster completion time
* Results from preliminary internal testing (kratai v.1.9.4 vs no skill baseline). Actual results may vary depending on task complexity and agent behavior.
Key Features
From architecture truth to AI integration — see how it works
Built-in Coding Agent & SKILL
Pre-configured SKILL teaches AI to follow your design principles automatically. Local MCP server provides direct access to architecture data — no manual setup required.
- ✓Automatic pattern recognition
- ✓Follows KISS, DRY, SRP principles
- ✓Minimal lines of code, high cohesion
AI Understands Your Architecture
AI agents query your architecture before generating code. No expensive context dumps — AI gets structured, accurate system information through MCP server.
- ✓Direct access to living architecture diagrams
- ✓Massive token reduction vs raw file dumps
- ✓No hallucinations, always accurate
Create Different Architectural Views
Save different perspectives for different needs — focus on domains, API layers, or specific features. Each diagram is a lens into your system structure.
- ✓Multiple saved configurations
- ✓Switch instantly between views
- ✓Git diff highlighting
Fine-Grained Control
Choose exactly what to show — select folders, filter relationship types, and control class types. Tailor each diagram to your specific needs.
- ✓24 relationship types to filter
- ✓4 class types (Class, Interface, Module, Other)
- ✓Click to jump directly to code
Detailed Features
Accurate architecture diagrams, AI integration, and multi-language support
AI Integration via SKILL & Local MCP Server
Architecture-Aware SKILL
Pre-configured skill teaches AI to analyze existing patterns and follow your design principles automatically. No manual prompting required.
Local MCP Server
Built-in Model Context Protocol server gives AI agents direct access to your architecture diagrams. AI can query your system structure before generating code, understanding the full context.
Software Engineering Principles
Ensure coding AIs consider foundational software engineering principles (KISS, DRY, SRP, high cohesion, low coupling) to produce minimal lines of code and maintain architectural integrity.
Architecture Intelligence
Deterministic Analysis
Generate interactive architecture diagrams directly from your codebase using static analysis. No LLM tokens required, no hallucinations, always reflects the actual code structure.
Single Source of Truth
Diagrams represent the real state of your system, making it easy for developers to understand the overall architecture and reducing token costs when AI agents need context.
Developer-Friendly Navigation
Git diff highlighting shows uncommitted changes at a glance. Click any element to jump directly to the code.
Language & Framework Support
TypeScript
Generics, decorators, interfaces, React/NestJS patterns
JavaScript
ES6 classes, JSX, JSDoc annotations, React hooks
Python
Type hints, async/await, protocols, dataclasses
Java
Spring Boot, JPA, REST APIs, dependency injection
PHP
PHP 7.4+/8.0+, Laravel/Symfony, traits
Spring Boot
Controller→View, JPA relationships, REST endpoints, DI
Django
View→Template, ORM relationships, REST Framework
Next.js
Component rendering, Type/DTO relationships, API routes
Coming Soon
React, Laravel, and Symfony framework enrichment
Spec-Driven Development
Architecture and specifications as the foundation for AI-assisted development
Spec-Driven Development (SDD) represents a shift in how software is built with AI. Instead of starting with code and hoping the architecture and behavior emerge correctly, SDD treats specifications and architecture as the primary artifacts that guide development.
Traditional AI-assisted workflows often lead to:
- ⚠️Inconsistent architectural decisions
- ⚠️Difficulty understanding the overall system structure
- ⚠️Growing technical debt as AI-generated code accumulates
Spec-Driven Development addresses this by making both what the system should do (specification) and how it should be structured (architecture) explicit and actionable. This creates a stronger foundation for AI agents to work from, resulting in more predictable, maintainable, and scalable outcomes.
kratai's Role in SDD
kratai contributes to this approach by giving developers clear visibility and oversight over architectural decisions as they build with AI. It helps you understand how your system is structured, how changes impact that structure, and how to keep architectural intent aligned with implementation — even as AI generates large portions of the codebase.
Ready to Visualize Your Code?
Install Kratai from the VS Code Marketplace and build your first perspective today