Stanford Researchers Release Paper2Agent: Turning Research Papers Into AI Agents That Reproduce Results and Run on New Data
Stanford researchers have developed Paper2Agent, a system that converts scientific papers and their codebases into AI-executable agents. This tool allows AI models to reproduce research results and run methods on new data automatically via the Model Context Protocol.
Why it matters
It addresses the 'reproducibility crisis' in science by automating the complex process of setting up and running code from academic publications.
Computational papers ship code that readers must clone, install, configure and debug. That cost keeps useful methods locked inside PDFs. A Stanford team led by Jiacheng Miao and James Zou proposes a fix. Paper2Agent was published in Nature on 16 September 2026. It converts a paper and its codebase into a Model Context Protocol (MCP) server. Any MCP-compatible agent, such as Claude Code , can then run the paper’s methods through natural language. The authors describe the result as a virtual corresponding author.
Is it deployable? Yes. The code is MIT-licensed and installs as a skill for Claude Code or Codex. Prebuilt AlphaGenome , Scanpy and TISSUE servers run on Hugging Face Spaces. A hosted version is also available at paper2agent.ai .
Paper2Agent runs on Claude Code’s agent SDK. A central orchestrator dispatches specialized sub-agents through 6 steps:
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