AGE-nt aggregates data from 11 sources (PubMed, clinical trials, grants, patents and more), normalises the documents into a unified schema, and exposes reasoning tools for evidence grading, trajectory analysis and gap identification through an MCP server, so any LLM client can connect to it.
AGE-nt was built in 24 hours for the Berlin Bio × AI Hackathon (27–28 February 2026), organised by Nucleate Germany, and was selected for the final pitch showcase.
The core idea is that the data layer is the product: agents can only reason well about ageing interventions if the evidence from papers, trials, grants and patents is retrieved, normalised and graded in a consistent way. AGE-nt provides that layer together with tools for grading evidence, tracking how an intervention’s evidence base has evolved over time, and identifying gaps.
Built with Python, FastAPI, FastMCP and a React/TypeScript frontend.