We introduce Paper2Agent, an automated framework that converts research
papers into AI agents. Paper2Agent transforms research output from passive
artifacts into active systems that can accelerate downstream use, adoption, and
discovery. Conventional research papers require readers to invest substantial
effort to understand and adapt a paper’s code, data, and methods to their own
work, creating barriers to dissemination and reuse. Paper2Agent addresses this
challenge by automatically converting a paper into an AI agent that acts as a
knowledgeable research assistant. It systematically analyzes the paper and the
associated codebase using multiple agents to construct a Model Context Protocol
(MCP) server, then iteratively generates and runs tests to refine and robustify
the resulting MCP. These paper MCPs can then be flexibly connected to a chat
agent (e.g. Claude Code) to carry out complex scientific queries through
natural language while invoking tools and workflows from the original paper. We
demonstrate Paper2Agent’s effectiveness in creating reliable and capable paper
agents through in-depth case studies. Paper2Agent created an agent that
leverages AlphaGenome to interpret genomic variants and agents based on ScanPy
and TISSUE to carry out single-cell and spatial transcriptomics analyses. We
validate that these paper agents can reproduce the original paper’s results and
can correctly carry out novel user queries. By turning static papers into
dynamic, interactive AI agents, Paper2Agent introduces a new paradigm for
knowledge dissemination and a foundation for the collaborative ecosystem of AI
co-scientists.