Robin (FutureHouse)
First multi-agent system to integrate hypothesis generation with experimental data analysis in a lab-in-the-loop workflow. Built on the Aviary framework, validated wet-lab in dry age-related macular degeneration.
| Affiliation | FutureHouse, with collaborators at the University of Oxford and Fordham University (FutureHouse) |
| First introduced | 2025-05 (preprint); peer-reviewed paper accepted 2026-05 |
| Lifecycle stages | Multi-stage (hypothesis generation + experiment design + analysis; lab-in-the-loop) |
| Autonomy level | Semi-autonomous (closed-loop with checkpoints; humans run wet-lab experiments) |
| Domain focus | Biology / therapeutics |
| Availability | Open source (code) + closed agent platform (FutureHouse-hosted Crow / Falcon / Finch endpoints) |
Approach
Multi-agent Jupyter notebook built on the FutureHouse Aviary framework. Combines PaperQA2-based literature search agents (Crow for concise, Falcon for deep summaries) with Finch, a Jupyter-native data analysis agent that runs analyses across multiple independent trajectories and produces consensus conclusions.
Validation
Wet-lab study on dry age-related macular degeneration (dAMD). Robin proposed enhancement of retinal pigment epithelium phagocytosis as a strategy, selected drug candidates, analyzed RNA-seq follow-up, and produced all hypotheses, analyses, and figures in the main text.
Notable results
- Identified ripasudil (a clinically used ROCK inhibitor) as a novel dAMD candidate, confirmed in vitro.
- Identified KL001 as a second hit.
- Proposed and analyzed an RNA-seq follow-up that revealed ABCA1 upregulation as a candidate mechanism/target.
Primary paper
Ghareeb et al., “A multi-agent system for automating scientific discovery,” Nature 2026.