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.

A Twitter thread laying out concrete steps to build a working AI agent from scratch, skipping theory and focusing on actual implementation you can follow in sequence.

• Provides a step-by-step guide designed for beginners with no prior agent-building experience
• Emphasizes practical, hands-on instructions rather than conceptual background
• Aims to produce a functional agent that operates independently without constant user intervention