Paper2Agent Converts Scientific Papers Into Interactive AI Agents

"A research team from Stanford University has released Paper2Agent, a framework that automatically converts scientific papers into interactive AI agents. The system, introduced in a recent paper, aims to make research methods more accessible by transforming traditional publications into dynamic entities that can execute analyses, reproduce results, and respond to new scientific queries through natural language interaction. Paper2Agent builds on the Model Context Protocol (MCP), a standard that allows large language models to connect with external tools and datasets."

"In contrast to the static nature of most research papers, which require significant technical effort to reproduce, Paper2Agent seeks to reduce barriers to experimentation. The system handles environment setup, dependency management, and tool execution, producing validated, reproducible outputs. According to the authors, the framework operates autonomously, requiring minimal human input beyond providing a paper's repository link. Processing times range from 30 minutes to several hours, depending on the complexity of the codebase."

Paper2Agent is a framework that automatically transforms scientific papers into interactive AI agents capable of executing analyses, reproducing results, and answering natural-language queries. It builds on the Model Context Protocol (MCP) to connect large language models with external tools and datasets. The framework identifies a paper's codebase, extracts methods, and exposes them as callable tools through an MCP server which chat agents can invoke. The system automates environment setup, dependency management, and tool execution to produce validated, reproducible outputs with minimal human input. Processing times vary from 30 minutes to several hours.

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