The Agentic Scientist – SakanaAI

Turn a task description into experiments, ablations, and a paper, driven by coding agents.

Agentic Scientist treats coding agents as AI scientists. Starting from a task.md, the agents propose ideas, run experiments, evaluate outcomes against the task rubric, and iterate toward stronger programs. The pipeline then runs baselines and ablations, a sealed final evaluation, post-hoc analyses, and writes the paper, with every stage kept under one run directory.

The recommended setup runs every agent phase inside Docker, which is the default for agsci run. Install Docker, then build the agent image:

docker build -t agentic-scientist/agent-cli:latest -f src/docker/agent-cli/Dockerfile .

If the build cannot reach package mirrors, add --network=host to the docker build command.

Security note — Agentic Scientist launches coding agents with full tool permissions (Headless --allow yolo). They can run arbitrary commands and inherit the API keys you give them, and the task files and papers they read steer what they do. Keep runs inside the Docker image rather than on your workstation, only run tasks and sources you trust, and review run directories before sharing them: agent logs can echo document contents and paths.

Python ≥ 3.11 with uv .uv sync creates.venv and installs theagsci command:

curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync
source .venv/bin/activate

API keys in a.env file at the repo root (ANTHROPIC_API_KEY ,OPENAI_API_KEY ,GEMINI_API_KEY , ... whichever your agent needs). The pipeline mounts it into the agent containers. Never commit it. #

Agent CLIs are already inside the image. For native (non-containerized) runs, install the CLI of the agent you use plus Git and Node.js; versions and commands are indocs/install.md . #

GPUs are required only by the GPU task presets. The pipeline discovers them throughnvidia-smi and refuses to start compute phases unless the visible GPUs match the preset. #

Autoillustrate (optional) is built into the image for concept figures. To use it directly on the host, follow the single-command install in theAutoillustrate README .

Run agsci doctor to check the host, or agsci doctor examples/<task> preset=<task> to check everything one specific run needs (CLI, credentials, image, GPUs, task scaffold).

Point agsci run at a task directory. This runs the default six phases (task drafting → investigation → ablations → final evaluation → post-hoc analysis → paper) on the CPU-only circle-packing example:

agsci run examples/circpack

Add --dry-run to validate the configuration and preview the exact commands without running anything. Every option is a Hydra key=value override; agsci run --help lists them all.

Note — Agentic Scientist drives claude, codex, cursor, opencode, gemini, and pi through Headless CLI, so the same experiment can be repeated across agents and models: pick one with agent.name=<agent> agent.model=<model>.

Task drafting only writes a scaffold when the task directory has none; every shipped example already includes its evaluator, so the drafting phase only snapshots it and the investigation phase builds and validates the task runtime. Drafting a task from a bare research brief is experimental (see example 8).

Each run writes one predictable envelope under results/:

results/<task>/<agent>/<run_id>/
├── task_drafting/ # drafting logs and validation audit
├── task/          # immutable run-local task snapshot
├── investigation/  # ideas, attempts, winner, and optimizer memory
├── ablations/     # baselines and ablations
├── final_evaluation/         # sealed final evaluation
├── posthoc/       # post-hoc analyses
└── paper/         # manuscript, PDF, manifest, and optional AI reviews

Investigation plus the task snapshot is enough to write a paper; the other phases add evidence when available and are reported as degraded when they fail. Browse any run in the browser with agsci inspect start (http://127.0.0.1:8765).

The default configuration is the one to start with: the claude agent, every phase in Docker, and the CPU-only examples/circpack task. It needs a Claude login, no GPUs, and exercises every phase through to a compiled paper.

Build the image and the Python environment as described inInstall . 2. Create .env at the repo root. Only the selected agent's keys are forwarded into its containers:

CLAUDE_CODE_OAUTH_TOKEN=...   # claude, billed to your Claude subscription (ANTHROPIC_API_KEY for pay-per-token)
OPENAI_API_KEY=...            # recommended: concept figures in the paper and the `review` phase

Other agents: codex readsOPENAI_API_KEY orCODEX_API_KEY ,gemini readsGEMINI_API_KEY ,cursor readsCURSOR_API_KEY , and Bedrock-backed Claude reads theAWS_* variables. Concept figures useOPENAI_API_KEY , fall back toGEMINI_API_KEY , and otherwise ship a plain export. 3. Export host variables only if you need them. Everything else (host networking, the image, the.env mount) is already theagsci run default:

export AGENTIC_TEMP_ROOT=/path/to/scratch       # existing writable dir on a large disk; default: $TMPDIR, /mnt/localssd, or /tmp
export AGENT_CONTAINER_GPUS=device=<UUID,...>   # GPU presets only: which GPUs to use; default: the first `container.gpu_count` (x investigation workers) from nvidia-smi

Check, preview, then run. Shrinking the ablation and post-hoc budgets makes the first end-to-end run finish faster without skipping a phase:

agsci doctor examples/circpack          # host, image, credentials, task scaffold
agsci run examples/circpack --dry-run   # print every phase command, run nothing
agsci run examples/circpack \
  investigation.execution.max_idea_cycles=2 \
  ablations.execution.num_replications=1 \
  ablations.execution.max_evals_per_variant=2 \
  posthoc.execution.max_selected_analyses=1

The paper lands in results/circpack/claude/<run_id>/paper/paper.pdf ;agsci inspect start browses the run. Drop the overrides for the default budgets (3 idea cycles, 5 ablation replications, 3 post-hoc analyses), then move on to the GPU task preset and a pinned model, for exampleagsci run examples/grpo preset=grpo agent.model=claude-sonnet-5 .

Task presets under configs/preset/ hold the campaign budgets, timeouts, paper format, and required GPUs. configs/preset/paper.yaml is the shared campaign preset and configs/preset/grpo.yaml composes it for the GRPO task (two GPUs, harness-side GPU evaluation):

agsci run examples/grpo preset=grpo

Explicit overrides win over the preset:

agsci run examples/grpo preset=grpo \
  agent.name=opencode \
  agent.model=amazon-bedrock/global.anthropic.claude-opus-5-5 \
  investigation.execution.max_idea_cycles=20

Every phase resumes against the run directory. The pipeline recovers the original idea-cycle budget; pass the original agent and model again:

agsci run examples/circpack execution.phases=ablations,posthoc,paper \
  paths.run_dir=results/circpack/claude/20260331_094917

agsci run examples/circpack execution.phases=paper \
  paths.run_dir=results/circpack/claude/20260331_094917

Adding the review phase sends the complete manuscript to the OpenAI-backed reviewer. Review failures are recorded but never discard the paper:

agsci run examples/circpack paper.paper_format=iclr \
  execution.phases=task_drafting,investigation,ablations,final_evaluation,posthoc,paper,review

The same reviewer runs standalone on any PDF with agsci review. It reads OPENAI_API_KEY from the environment or the repo .env and prints the review JSON:

agsci review results/circpack/claude/<run_id>/paper/paper.pdf --output review.json

Each phase composes its stage's own config, so any stage key is reachable as <phase>.<key>:

agsci run examples/circpack \
  investigation.execution.max_idea_cycles=5 \
  ablations.execution.num_replications=1 \
  posthoc.execution.max_selected_analyses=2 \
  paper.paper_format=iclr paper.paper_review_rounds=1

agsci investigate (src/investigation/cli.py) runs the idea/experiment loop on its own, natively by default, optionally with parallel workers:

agsci investigate agent.name=claude paths.task_dir=examples/circpack \
  execution.workers=2 execution.max_idea_cycles=4

See docs/usage.md for the standalone investigation loop, its container mode, and every argument.

examples/grpo ships its own evaluator and needs two GPUs: vLLM sampling on one, training on the other. Fetch the model weights once (about 3 GB, into examples/grpo/resources/), then declare the GPUs the task needs:

uv run examples/grpo/download_model.py
agsci run examples/grpo container.gpu_count=2 \
  investigation.execution.harness_evaluated_attempts=true \
  investigation.execution.gpu_evaluation=true

To explore ideas in parallel, add investigation workers. Each worker gets its own container.gpu_count slice, so two workers need four visible GPUs:

agsci run examples/grpo container.gpu_count=2 investigation.execution.workers=2 \
  investigation.execution.harness_evaluated_attempts=true \
  investigation.execution.gpu_evaluation=true

Each evaluation trains for 1-2 hours; raise investigation.execution.harness_evaluation_timeout_seconds above its 7200 s default if the harness cuts runs short.

A task directory holding only a task.md brief makes the drafting phase write the initial program, evaluators, and task contract itself:

agsci run examples/circpack_task_drafting execution.phases=task_drafting

This works for simple tasks such as circle packing but is not yet robust: for larger tasks like the world-model or tabular distribution-shift examples it may fail to produce a scaffold that matches the full pipeline contract. Start from a shipped scaffold for anything beyond a small task, and check the drafting audit under task_drafting/ when a draft is rejected. See docs/task_drafting.md.

Six phases run in sequence: Task drafting → Investigation → Ablations → Final evaluation → Post-hoc analysis → Paper, with optional Review and Revision phases afterwards. Task drafting (experimental for tasks without a shipped scaffold) turns task.md into a sealed evaluator and rubric; investigation is the agentic idea → experiment → evaluate loop; ablations compare the winning program against baselines and ablated variants; the final evaluation re-scores it in a sealed environment; post-hoc analysis mines the artifacts for extra evidence; and the paper phase writes, compiles, and audits the manuscript against that evidence. Each phase runs in its own container and only ever reads the run-local snapshot, so results stay reproducible and examples/ is never modified. See docs/pipeline.md for the details.

• Installation — agent CLI, Headless runtime, Docker build options, credentials, container options, Autoillustrate.
• End-to-end pipeline —agsci run recipes, presets, GPUs, resuming, ablations, and cost output.
• Investigation loop usage — standalone loop, parallel workers, containers, arguments, and checkpoints.
• Task drafting — how tasks become sealed evaluators, qualification, and repair.
• Variants pipeline — stages, custom baselines, cross-task generalization, output layout.
• Post-hoc pipeline — standalone architecture and resume flow.
• Run Inspector — launch modes and overrides for the web UI.
• Script reference — the individual agent scripts and their outputs.
• Agent billing ,temp storage ,GPU quarantine ,evaluation evidence ,paper final outcomes ,no-winner studies .

If you use Agentic Scientist in your research, please cite our paper:

@article{agentic_scientist,
  title   = {The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery},
  author  = {Lange, Robert Tjarko and Grillotti, Luca and Yamada, Yutaro and Charakorn, Rujikorn and Ha, David},
  journal = {arXiv preprint arXiv:2610.18933},
  year    = {2026}
}