Banani

Banani is a web-based UI screen reference library that collects and organizes interface screenshots from popular consumer applications and layers AI-powered editing and wireframe generation on top of them. The platform is positioned as a free alternative to paid design reference tools in the AI Design Assistant space.

According to Banani's own reference documentation, the platform covers screens from at least ten well-known apps: Tinder, Airbnb, Pinterest, ChatGPT, Things 3, AllTrails, Reddit, Perplexity, Substack, and Duolingo. This span across social networking, travel, visual discovery, AI assistants, task management, outdoor activities, community discussion, knowledge search, content publishing, and education categories gives designers a broad palette of real-world UI patterns to study.

The core workflow on Banani follows three steps: browse and select a UI screen from the reference library, copy or download it, then use AI to modify the screen by describing desired changes in natural language. A separate capability allows users to upload a screen and generate wireframe versions — a feature that could accelerate early-stage design exploration by converting high-fidelity references into structural skeletons.

Screen organization mirrors professional design reference tools. Within each app, screens are grouped into logical user flows. Airbnb's reference section splits its screens across Booking (4 screens), Listing Details (7 screens), Reviews (3 screens), Trip and Reservation Details (4 screens), and Profile (2 screens). Tinder's screens are arranged by Onboarding Flow, with Launch Screen and Welcome Screen as distinct entries. Pinterest's catalog includes Account and Notifications, Saving Pin, Profile, and Search flows. This flow-based taxonomy helps designers locate specific interface patterns without scrolling through undifferentiated screenshot dumps.

On the AI front, Banani mentions an AI agent component in its Tinder reference directory, though the documentation does not elaborate on capabilities, underlying model, or degree of autonomy. The AI editing feature is described consistently across app reference pages — select a screen, type changes, get edited output — but the available material stops at the feature-name level. There is no public information on whether edits are applied at the pixel level or the component level, whether the system preserves design-system constraints, or how ambiguous instructions are resolved.

The platform's public-facing pages use explicit competitive positioning. Screens like "Tinder Launch Screen UI | Free Mobbin Alternative" name the incumbent directly, signaling a strategy built on price accessibility and AI differentiation rather than catalog breadth alone.

For tools that overlap with specific design tasks, Banani's AI editing capability places it alongside tools like AI Remove Text From Image, which handles a narrower but related image manipulation task, and InkStudio.ai, another AI-enabled creative tool. These comparisons are loose — Banani targets UI design workflows specifically, while the alternatives address different segments of the broader AI-assisted creation landscape.

What the available source material does not resolve: how frequently screens are refreshed as apps ship new versions, whether the free tier has usage caps or an eventual monetization path, how well AI editing handles complex UI with overlapping elements, and whether wireframe generation produces designer-ready outputs or rough approximations. These open questions represent the gap between a promising feature list and a vetted daily driver.