Review of Tools for Zero-Code LLM Based Application Development
Priyaranjan Pattnayak, Hussain Bohra

TL;DR
This survey reviews recent zero-code platforms leveraging LLMs for application development, categorizing their features, comparing their strengths and limitations, and discussing future directions to democratize AI-powered app creation.
Contribution
It provides a comprehensive taxonomy and comparison of zero-code LLM platforms, highlighting their core features, trade-offs, and future research directions.
Findings
Zero-code LLM platforms lower barriers to AI app creation.
Challenges remain in flexibility and reliability of these platforms.
The landscape is rapidly evolving with new opportunities.
Abstract
Large Language Models (LLMs) are transforming software creation by enabling zero code development platforms. Our survey reviews recent platforms that let users build applications without writing code, by leveraging LLMs as the brains of the development process. We adopt a broad survey methodology, categorizing platforms based on key dimensions such as interface style, backend integration, output type, and extensibility. We analyze both dedicated LLM based app builders (OpenAI's custom GPTs, Bolt.new, Dust.tt, Flowise, Cognosys) and general no code platforms (e.g., Bubble, Glide) that integrate LLM capabilities. We present a taxonomy categorizing these platforms by their interface (conversational, visual, etc.), supported LLM backends, output type (chatbot, full application, workflow), and degree of extensibility. Core features such as autonomous agents, memory management, workflow…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Software Engineering Research · Machine Learning and Data Classification
