Kombai
AI Figma-to-code generator
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About Kombai
What this tool does and how it can help you
AI tool that converts Figma designs into high-quality frontend code (React, HTML/CSS).
Prompts for Kombai
Challenges using Kombai
Key Capabilities
What you can accomplish with Kombai
UX Interpretation Engine
Advanced AI engine that accurately translates Figma designs, images, and text inputs into production-ready frontend code using custom ensemble AI models for higher precision than standard LLMs
Multi-Framework Code Generation
Generates clean, backend-agnostic frontend code supporting 25+ libraries including React, Next.js, Material-UI, Chakra UI, and Mantine with high library-specific accuracy
IDE Integration
Operates directly inside the developer's IDE, providing seamless integration with existing development workflows and tooling
Tool Details
Technical specifications and requirements
License
Freemium
Pricing
Freemium / Subscription
Supported Languages
Feature Highlights
Detailed features and capabilities
UX Interpretation Engine
Advanced AI engine that accurately translates Figma designs, images, and text inputs into production-ready frontend code using custom ensemble AI models for higher precision than standard LLMs
Multi-Framework Code Generation
Generates clean, backend-agnostic frontend code supporting 25+ libraries including React, Next.js, Material-UI, Chakra UI, and Mantine with high library-specific accuracy
IDE Integration
Operates directly inside the developer's IDE, providing seamless integration with existing development workflows and tooling
Editable Development Plans
Provides AI-generated, editable development plans for complex frontend tasks, allowing developers to review and modify the approach before execution
Auto Error Fixing
Automatically detects and fixes TypeScript, compilation, and runtime errors in generated code, ensuring code quality and reducing debugging time
Preview Server Integration
Runs preview servers to validate generated code in real-time, allowing developers to see results immediately
Contextual Repository Understanding
Uses purpose-built RAG layers to detect and use appropriate context from existing repositories, ensuring generated code aligns with project conventions
Tech Stack Adaptation
Adapts to developer's preferred technology stack including various CSS libraries, state management tools, and frontend frameworks