We present app.build (https://github.com/neondatabase/appdotbuild-agent), an open-source framework that improves LLM-based application generation through systematic validation and structured environments. Our approach combines multi-layered validation pipelines, stack-specific orchestration, and model-agnostic architecture, implemented across three reference stacks. Through evaluation on 30 generation tasks, we demonstrate that comprehensive validation achieves 73.3% viability rate with 30% reaching perfect quality scores, while open-weights models achieve 80.8% of closed-model performance when provided structured environments. The open-source framework has been adopted by the community, with over 3,000 applications generated to date. This work demonstrates that scaling reliable AI agents requires scaling environments, not just models -- providing empirical insights and complete reference implementations for production-oriented agent systems.
@article{arxiv.2509.03310,
title = {app.build: A Production Framework for Scaling Agentic Prompt-to-App Generation with Environment Scaffolding},
author = {Evgenii Kniazev and Arseny Kravchenko and Igor Rekun and James Broadhead and Nikita Shamgunov and Pranav Sah and Pratik Nichite and Ivan Yamshchikov},
journal= {arXiv preprint arXiv:2509.03310},
year = {2026}
}