Autonomous AI scientists
Named AI systems that take meaningful initiative in hypothesis generation, experiment design, or analysis. Two recent peer-reviewed Nature papers (Google’s Co-Scientist, FutureHouse’s Robin) anchor the wet-lab end of the landscape; general-purpose biomedical agents (Biomni, CRISPR-GPT) and chemistry agents (Coscientist, ChemCrow) cover the rest of the field.
This section is informational — a map of the field for reference and comparison. These are external systems, not components the Composer installs or runs; instead, the Composer can assemble an equivalent multi-agent system from cataloged Skills and MCP servers. Where a system also ships as an installable Claude component, it additionally appears in the Catalog (e.g. Biomni as a Claude Skill).
Where to start
- Landscape — what’s currently working, how systems are evaluated, and the open problems.
- Systems — one page per named system, with affiliation, autonomy level, validation, availability, and citations.
What “autonomous” means here
Autonomy here is a spectrum:
- Assistive — human-in-the-loop, agent suggests, human decides.
- Semi-autonomous — closed-loop with checkpoints.
- Fully autonomous — closed-loop without intervention.
The three primary lifecycle stages are hypothesis generation, experiment design, and analysis. A system may cover one stage, several, or close the full loop.
See also
- Contribute — file a recipe question if you want to use one of these systems on a concrete problem and aren’t sure how.