Paper2Agent is an AI agent that reads scientific papers and automatically reproduces their results. It's published in Nature and available as a live demo where you can query it about papers and run workflows through GitHub.

• Automates the extraction and reproduction of experimental results directly from published papers
• Reduces manual work scientists spend reverse-engineering methods and validating findings
• Deployed as an interactive agent you can query in real-time about paper contents and methodology

Paper2Agent is a system that automatically transforms research papers into functional AI agents by extracting code and methods into MCP servers. You can use it through a skill in Claude Code or Codex to convert any paper's codebase into interactive tools.

• The system coordinates parallel specialist agents to extract scientific papers into reliable MCP servers with minimal manual setup
• You install the skill, point it at a paper URL and code repository, and it generates tested MCP tools ready to connect to your coding agent
• Three working examples (AlphaGenome, TISSUE, Scanpy) show agents handling genomic analysis, spatial transcriptomics, and single-cell preprocessing tasks with specific scientific queries

Paper2Agent automatically transforms static research papers into interactive AI agents that users can query in natural language, eliminating the need to manually install code, configure environments, or parse technical documentation. The system wraps a paper's methods, code, and data as an MCP (Model Context Protocol) server that connects to LLMs like Claude, letting researchers apply the paper's techniques to new problems without programming expertise.

• Paper2Agent solves a real friction point: even well-documented computational methods require substantial setup work (installing dependencies, understanding APIs, configuring parameters), which blocks adoption by researchers without strong technical skills. The system lets users ask questions like "interpret this variant's effect on chromatin accessibility" instead of wrestling with repository setup.
• The framework validates reproducibility by testing each tool against the original paper's reported results and figures, then locks those tools to prevent LLM hallucination and ensure consistent outputs. Every tool includes a code reference back to the original paper for transparency.
• Demonstrated agents successfully reproduced results from AlphaGenome (genomic variant interpretation), Scanpy (single-cell analysis), and TISSUE (spatial transcriptomics), then performed novel analyses like collaborating across multiple agents to prioritize a causal gene for psoriasis.