TL;DR

For many professional frontend teams, Figma files are the primary source of design instructions.

Developers currently use image exports or Figma MCPs with existing agents, which often struggle with complex, real-world designs. These MCPs can also be context-inefficient, leading to model hallucinations.

In this guide, we will compare the best two AI tools for converting Figma design to code in minutes. These two AI tools are:

Kombai: a specialized AI agent which is purpose-built to “think” like a frontend developer, and convert Figma designs into high-quality UI code across a configurable, robust tech stack.

Framelink MCP: a protocol-based server that acts as a “universal translator,” and gives design context from Figma directly into your general-purpose AI coding assistant (e.g., Cursor, Claude, or VS Code).

Before we get started, here is what we will cover:

What is Kombai?

What is Framelink MCP?

Head-to-Head comparison of Kombai vs Framelink MCP

Testing workflows and code outputs of Kombai vs Framelink MCP

The final verdict on which tool is right for you

Here is a preview of the final results between Kombai and Framelink MCP.

What is Kombai?

Kombai is an AI agent purpose-built for frontend development. It generates beautiful, functional frontends from Figma, text, images, or code, understanding your codebase and best practices of 30+ frontend libraries.

Key features:

Multi-input support: Works with Figma designs, text instructions, images, or existing code

Tech stack aware: Supports 30+ frontend libraries including React, Next.js, Material UI, Tailwind, and more

Smart planning: Generates editable development plans before complex tasks

Codebase integration: Scans and reuses existing components and theme variables

Auto-fixing: Automatically fixes TypeScript and linting errors

Sandbox preview: Run and test generated code in a local environment

Two modes: Code mode for generation, Ask mode for codebase understanding

You can learn more about Kombai AI agent here onKombai docs.

What is Framelink MCP?

Framelink MCP (Model Context Protocol) is a server that bridges Figma design files with AI-powered coding tools, such as Claude Code, Cursor or GitHub Copilot on VS Code.

Key Features:

Context-Rich Generation: Moves beyond “screenshot-to-code” guesswork.

IDE Integration: Designed to live inside your AI-powered editor (Cursor, VS Code, Windsurf, etc.).

Developer-in-Control: The developer still drives the process via prompts, using the MCP as their “Figma expert.”

You can learn more about Framelink MCP here.

Now that you have learned what Kombai agent and Framelink MCP are, let us see how they compare in a head-to-head comparison.

Head-to-Head comparison of Kombai vs Framelink MCP

The difference between Kombai and Framelink MCP boils down to specialization versus flexibility and workflow integration.

Kombai significantly outperforms generic coding agents + Frontier models + MCPs in building UX from Figma, image, or text based prompts as shown below inbenchmarks for real-world frontend tasks.

Here is the head-to-head comparison of the Kombai agent and the Framelink MCP.

Feature	Kombai Agent	Framelink MCP
Functionality	Generates code directly.	Provides context to another AI tool (which then generates code).
Code Output	Highly Configurable (React, Next.js, support for multiple UI libraries (MUI, Shadcn, etc.), and advanced styling options).	Depends entirely on your AI assistant (React, Vue, Svelte, Angular, etc.)—if the LLM knows it, the MCP can enable it.
Figma Reliance	Highly flexible; performs well with “messy” designs.	Benefits greatly from well-structured files (Dev Mode, variables, components).
Workflow	Generate in a sandbox, then copy/paste into the project.	Prompt in editor, AI writes code directly into the file.
Code Quality	Production-ready code with high compilation success but may need tweaks.	Depends on the quality of the connected LLM in your IDE and prompt quality.
Context Awareness	Can scan the local codebase to adhere to local styling conventions and theme files.	Can be instructed via prompt to use existing components and architecture.
Flexibility	Limited to Kombai’s comprehensive but defined supported tech stack.	Can be used for virtually any framework or language the connected LLM supports.

Testing workflows and code outputs of Kombai vs Framelink MCP

In this section, we will convert a Figma design into frontend code in two scenarios. In the first scenario, we will use Kombai extension on a code editor of your choice to convert a Figma design into frontend code.

Then in scenario two, we will use Framelink MCP on a code editor of your choice to provide a Figma design as context to an AI coding tool to generate frontend code using a stack of your choice.

After that, we will compare the results from both scenarios and determine which tool is better at converting Figma designs into pixel perfect frontend UI.

Here is the Figma design we will be using in both scenarios. You can get it here on the Figma community.

Let us get started.

Scenario 1: The Kombai Figma Design-to-Code Workflow

To get started with Kombai agent, follow the set up guide in the Kombai docs, as shown below.

Once you have set up Kombai, follow the steps below to convert a Figma design into frontend code.

Let us jump in.

Step 1: Connect Figma

To add a Figma design as context, you need to connect your Figma account with Kombai. This lets Kombai access your Figma design and generate code based on them.

Here is how to connect your Figma account to Kombai:

Step 2: Add Figma design URL

Once you have connected your Figma account, copy the Figma design link, as shown below.

Then in the Enter the link to your Figma frame modal, add the Figma URL and click Confirm, as shown below.

Step 3: Configure your tech stack

After adding your Figma design URL, configure your tech stack. Kombai offers a wide range of tech stack. This lets you generate code that aligns with your workspace’s requirements.

To configure your tech stack, follow the steps as shown below and then save the configuration.

Step 4: Plan for complex tasks

After configuring your tech stack, you can plan for complex tasks. Kombai always generates an editable plan for you to approve or update. This ensures that the task and generated code is always in your control.

Complexity of a task is determined by the number of sections, images, icons, global store entities, queries, etc. required in the code.

You can plan for complex tasks, as shown below.

Once the plan is generated, click the Approve Plan & Start Coding button to accept the plan and start the code generation process, as shown below.

Step 5: Generate Code

Once the plan is approved or updated, Kombai starts the code generation process in the workset. Working set refers to the set of files generated by Kombai during the code generation process, as shown below.

Step 6: Run and fix code errors

After generating the code, Kombai will run the development server and fix any errors, as shown below.

Once the development server is running, navigate to the localhost URL in the terminal to preview the frontend code UI in the browser, as shown below.

As you can see from the preview results, Kombai was able to generate frontend code that is pixel perfect with the Figma design. The code only requires minimal tweaks to match the desired UI design.

Scenario 2: The Framelink MCP Figma Design-to-Code Workflow

To get started with Framelink MCP, follow the set up guide in the Framelink docs, as shown below.

Once you have set up Framelink MCP, follow the steps below to convert a Figma design into frontend code.

Let us jump in.

Step 1: Copy a link to a Figma frame

The MCP server compresses the data it receives from the Figma API by almost 90%. Even still, complex designs can overwhelm AI agents with too much information.

To do that, right click on the frame or group you’d like to implement, then select Copy/Paste as and choose Copy link to selection, as shown below.

Step 2: Paste the link into your editor

Once you have a link to the Figma frame or group, you can make a request to your editor’s AI agent.

A request as simple as “Implement this Figma frame” along with the link should be enough to get the agent to call the MCP’s get_figma_data function, but more context is often helpful in getting the best results, as shown below.

Step 3: Generate Code

After submitting your request, your coding agent will get to work. It should call the MCP’s get_figma_data function with the link you provided and start the code generation process, as shown below.

Step 4: Run and fix code errors

After generating the code, your coding agent will run the development server and fix any errors, as shown below.

Once the development server is running, navigate to the localhost URL in the terminal to preview the frontend code UI in the browser, as shown below.

As you can see from the preview results, the coding agent was not able to generate frontend code that is pixel perfect with the Figma design. The code requires a lot of tweaks to match the desired UI design.

Conclusion: Which Tool is Right for You?

In conclusion, both Kombai and Framelink MCP offer powerful solutions for converting Figma designs into code, but they cater to different needs and workflows.

Kombai is a specialized AI agent that excels at generating high-quality, production-ready UI code directly from Figma designs.

Its strengths lie in its configurable tech stack, its ability to handle complex and even “messy” designs, and its feature of generating an editable development plan before starting complex tasks.

The Kombai agent is the ideal choice for developers and teams who prioritize a streamlined workflow, desire highly configurable code output, and want a tool that can intelligently interpret and convert intricate designs with minimal manual intervention.

Framelink MCP, on the other hand, acts as a “universal translator” that provides rich design context from Figma directly into your existing AI-powered coding assistant, such as those in Cursor, Claude, or VS Code.

Its primary advantage is its flexibility; it can be used with virtually any framework or language that your connected LLM supports.

The Framelink MCP is best suited for developers who prefer to remain in their familiar coding environment, want to maintain a high degree of control over the code generation process through prompting, and require the adaptability to work with a wide variety of technologies.

Ultimately, the decision between Kombai and Framelink MCP comes down to a choice between a specialized, end-to-end solution and a flexible, integrated tool.

If your priority is to get high-quality, production-ready code with a specialized tool, Kombai is the better option. If you value flexibility and want to enhance your existing AI-powered workflow with rich design context, Framelink MCP is the more suitable choice.