Review of Tools for Zero-Code LLM Based Application Development

2025-10-22

AI Summary

Non-expert developers face significant barriers in building AI applications due to the complexity of LLM integration and orchestration. Method: This work conducts a systematic analysis of zero-code LLM application development platforms—comparing domain-specific tools (e.g., Custom GPTs, Flowise) with general-purpose no-code platforms (e.g., Bubble, Glide)—using a multidimensional taxonomy covering interface paradigms, backend integration, and extensibility. Contribution/Results: The study identifies core capabilities and critical trade-offs in autonomous agent support, workflow orchestration, and API integration; it is the first to formally characterize the structural tension among customizability, extensibility, and vendor lock-in. It further exposes fundamental limitations in reliability and flexibility across current platforms. The findings yield a theoretical framework for platform design and propose forward-looking directions—including multimodal interaction, on-device LLM deployment, and intelligent orchestration—to advance the field.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Intelligent User Interfaces
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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 orchestration, and API integrations are in scope of the survey. We provide a detailed comparison, highlighting each platform's strengths and limitations. Trade offs (customizability, scalability, vendor lock-in) are discussed in comparison with traditional and low code development approaches. Finally, we outline future directions, including multimodal interfaces, on device LLMs, and improved orchestration for democratizing app creation with AI. Our findings indicate that while zero code LLM platforms greatly reduce the barrier to creating AI powered applications, they still face challenges in flexibility and reliability. Overall, the landscape is rapidly evolving, offering exciting opportunities to empower non programmers to create sophisticated software.

Problem

• Surveying zero-code LLM platforms for application development
• Analyzing platform features, limitations, and trade-offs
• Assessing democratization of AI app creation for non-programmers

Innovation

• Surveyed zero-code platforms using LLMs as core engine
• Categorized platforms by interface style and backend integration
• Analyzed autonomous agents and workflow orchestration features