SakanaAI/AI-Scientist-v2
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search 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
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
- readme: https://github.com/SakanaAI/AI-Scientist-v2 · fetched 2026-08-28 · 1603de9d0c26
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| SakanaAI/AI-Scientist-v2 | main | 46 |
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