Review of Tools for Zero-Code LLM Based Application Development
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Oct 2025.
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Citing papers per year: 2026: 4.
4 papers.
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- 2026MCPGen: Benchmarking LLMs on Executable MCPWorkflow Developmentcited by 0
- 2026MCPGen: Benchmarking LLMs on Executable MCPWorkflow Development20 Sep 2026
- 2026Is Vibe Coding the Future? An Empirical Assessment of LLM Generated Codes for Construction Safety14 Apr 2026
- 2026IndicJR: A Judge-Free Benchmark of Jailbreak Robustness in South Asian Languages18 Feb 2026
- 2026LLM-Guided Lifecycle-Aware Clustering of Multi-Turn Customer Support Conversations7 Jan 2026