Paper2Agent

Overview

Paper2Agent is an agentic system that automatically converts academic research papers into deployable AI agents, translating methodologies, algorithms, and experimental setups described in scientific literature into executable, tool-using agent workflows. It uses LLM-based code generation and structured parsing to bridge the gap between published research and working implementations. The system reduces the time from paper publication to prototype from weeks of manual engineering to hours of automated synthesis.

Why it matters

The bottleneck in applied AI has shifted from model capability to implementation velocity — turning frontier research into production code fast enough to stay competitive. Paper2Agent directly attacks that bottleneck by automating the translation layer between scientific literature and runnable agent systems, compressing what typically requires a senior ML engineer several days into an automated pipeline. For CTOs and research leads, this means faster experimentation cycles and a smaller gap between what frontier labs publish and what your team can actually ship. For investors, it signals a new class of infrastructure tooling that multiplies the leverage of small technical teams and is directly aligned with the one-person-unicorn thesis. If agentic AI is the next platform, Paper2Agent-style systems are the scaffolding that lets practitioners actually build on it.