Ross ROSS = Recommend OSS · open-source software intelligence for agents

SakanaAI/AI-Scientist-v2

The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search observed · 2026-08-28

github.com/SakanaAI/AI-Scientist-v2 · Python · NOASSERTION (other) observed · 2026-08-28

Health v2 · maintenance only

46/100

  • Activity 58
  • Release rhythm 35
  • Longevity 36

Flags: no_releases no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 512
  • days_rel: n/a
  • days_push: 257
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

7050 stars · 976 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

An end-to-end autonomous AI research system that generates hypotheses, runs machine learning experiments, analyzes data, and writes complete scientific manuscripts without human-authored templates. It employs progressive agentic tree search guided by an experiment manager agent and produced the first fully AI-written paper accepted through peer review at an ICLR workshop.

Use cases

  • automate scientific research end to end with AI
  • generate and test machine learning research hypotheses automatically
  • have an AI agent run experiments and write a paper
  • use agentic tree search for open-ended scientific discovery
  • automate the ML experiment and analysis pipeline
  • produce draft scientific manuscripts from an AI research idea
  • run autonomous ablation studies and data analysis

When to choose

  • You want fully open-ended ML research exploration without relying on predefined templates
  • You need a complete pipeline from idea generation through experiments to a written manuscript
  • You have a Linux machine with NVIDIA GPUs, LLM API keys, and a sandbox (e.g., Docker) for running LLM-written code

When to avoid

  • You have a well-defined task with a strong starting template and need high success rates - the v1 AI Scientist may perform better
  • You cannot safely sandbox execution of LLM-generated code, which poses security and process risks
  • You need automation for non-machine-learning scientific domains, which this system does not target
  • You lack a Linux/CUDA environment or cannot afford extensive LLM API usage

Facets

application · maturity active

agent-framework machine-learning deep-learning data-science workflow-automation llm-inference artificial-intelligence machine-learning large-language-models data-science python ai-scientist automated-scientific-discovery agentic-tree-search autonomous-research hypothesis-generation paper-writing llm-agents experiment-automation peer-review ai-agents automation linux gpu docker

1 source

Member repositories

RepositoryRoleHealth v2
SakanaAI/AI-Scientist-v2main46

For agents

markdown · JSON · MCP: product_card(name="SakanaAI/AI-Scientist-v2")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem