Krit vs Lovable: the generator and the design layer
This is not really a versus page, because these are two different jobs. Lovable builds you a working app from a prompt, and it is genuinely good at that. Krit is the design layer that makes an agent-built app look like someone meant it. Plenty of teams use both.
One tool builds the app, the other makes it look intentional
Lovable's job is zero to something. You describe an app, and you get a running full-stack thing with screens, a database, and auth. That compression of the first week of a project into an afternoon is real, and I am not going to pretend otherwise.
Krit's job starts where that ends. It is an infinite canvas where AI agents, Claude Code, Cursor, any MCP-capable agent, design real interactive screens against your brand, captured as rules the agent actually follows. You explore directions side by side, keep the screens consistent with each other, and export clean HTML/CSS, React, Next.js, or Vue when a screen is right. If the question is who scaffolds a backend, Lovable wins. If the question is who makes the product look designed, that is the job Krit exists for.
What matters
Krit
Lovable
What it makes
Designed screens, exported as clean frontend code
A working full-stack app from a prompt
How you steer design
Direct an agent on a canvas, compare directions side by side
Re-prompt and hope the next generation lands
Brand memory
Your brand captured as rules the agent designs against
Each prompt starts from the model's defaults
Consistency across screens
Held deliberately, screen after screen
Drifts as the app grows prompt by prompt
Where the output lives
Exported code you take into your own repo
An app inside Lovable's stack and hosting
Backend and data
Not the job, Krit is frontend design only
Database, auth, and hosting included
Who it is for
Technical teams shipping with AI agents, no designer
Anyone who wants a working app without writing code
Design ceiling
As high as your taste, iterated on a real canvas
Capped near what the model generates by default
Prompt-generated apps share a look, and buyers can tell
Here is the uncomfortable part. Apps generated by prompt tools converge on the same look: the same card layouts, the same gradients, the same spacing that is almost right, the same purple. It is not any one tool's fault, it is what happens when a model designs from its defaults. But your users have seen that look a hundred times now, and it reads as a signal: nobody sweated this.
That signal costs you. When a prospect lands on a screen that looks like every other AI-generated app, they discount everything behind it, the product, the team, the seriousness. The demo can be flawless and the impression is still a template. Fixing that is not a re-prompt away, because the problem is not one screen. It is the absence of a point of view across all of them.
Re-prompting is not design iteration
When a generated screen is close but wrong, the only lever a generator gives you is another prompt, and each generation is a bit of a dice roll. You can gain the fix and lose something else that was working. That loop is fine for getting to working, it is a bad loop for getting to good.
Krit gives the agent a canvas instead of a dice cup. Screens are real and interactive, you can put three directions next to each other, keep what works, and push on what does not. The agent designs against your brand rules, so a change is a decision that sticks, not a mutation you have to defend on the next generation. That is what iteration looks like in an actual design tool, just with an agent doing the drawing.
Brand is memory, and generators do not have one
A brand is not a logo, it is a set of decisions held over time. This gray, never that one. This type scale. This much restraint with color. A prompt-to-app tool cannot hold those decisions for you, because every prompt starts the negotiation over from the model's defaults.
In Krit your brand is captured as rules, and the agent designs inside them. Screen twelve matches screen one not because you policed it but because the rules were there when the agent drew both. That is the difference between an app that has a look and an app that had prompts. It is also why this works with whatever agent you already use: Claude Code, Cursor, anything that speaks MCP.
Many teams use both, and that is the honest answer
The pattern I actually see is not either-or. A team scaffolds with Lovable or another generator, or with their own agent in their own repo, and gets to a working product fast. Then the screens need to stop looking generated, and that is when they bring the work to Krit: redesign the surfaces on the canvas, hold them to one visual system, export the frontend code, and wire it in themselves.
So the fair scorecard is this. For getting a full-stack app running from nothing, Lovable is the better tool and I will say so plainly. For making that app look intentional, consistent, and yours, that is Krit's whole job, and a generator is not built to do it.
BE HONEST
When Lovable is the right call
If you are non-technical and want a real, working app without touching code, use Lovable. Krit assumes you or your agent will take exported code into a repo, and if that sentence is not your life, the generator is the honest recommendation.
Same answer if you are validating an idea over a weekend, if you need the backend included because standing up a database and auth yourself is the bottleneck, or if speed matters more than polish right now, which early on it often should. Krit earns its keep when the product is real and how it looks starts costing you: with users, with buyers, with investors. Before that point, ship the prototype and do not overthink it.
BUILD IT ON THE CANVAS
Tell your agent what to design. Watch it appear.
Stop drawing it by hand or designing it blind. Direct an agent on a real canvas and leave with code.