Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
| Date | Stars |
|---|---|
| 2026-07-31 | 6939 |
| 2026-08-02 | 6939 |
| 2026-08-05 | 6967 |
| 2026-08-06 | 6967 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<div align="center">
<a href="https://github.com/SakanaAI/AI-Scientist_v2/blob/main/docs/logo_v1.jpg">
<img src="docs/logo_v1.png" width="215" alt="AI Scientist v2 Logo" />
</a>
<h1>
<b>The AI Scientist-v2: Workshop-Level Automated</b><br>
<b>Scientific Discovery via Agentic Tree Search</b>
</h1>
</div>
<p align="center">
📚 <a href="https://pub.sakana.ai/ai-scientist-v2/paper">[Paper]</a> |
📝 <a href="https://sakana.ai/ai-scientist-first-publication/"> [Blog Post]</a> |
📂 <a href="https://github.com/SakanaAI/AI-Scientist-ICLR2025-Workshop-Experiment"> [ICLR2025 Workshop Experiment]</a>
</p>
Fully autonomous scientific research systems are becoming increasingly capable, with AI playing a pivotal role in transforming how scientific discoveries are made.
We are excited to introduce The AI Scientist-v2, a generalized end-to-end agentic system that has generated the first workshop paper written entirely by AI and accepted through peer review.
This system autonomously generates hypotheses, runs experiments, analyzes data, and writes scientific manuscripts. Unlike [its predecessor (AI Scientist-v1)](https://github.com/SakanaAI/AI-Scientist), the AI Scientist-v2 removes reliance on human-authored templates, generalizes across Machine Learning (ML) domains, and employs a progressive agentic tree search, guided by an experiment manager agent.
> **Note:**
> The AI Scientist-v2 doesn’t necessarily produce better papers than v1, especially when a strong starting template is available. v1 follows well-defined templates, leading to high success rates, while v2 takes a broader, more exploratory approach with lower success rates. v1 works best for tasks with clear objectives and a solid foundation, whereas v2 is designed for open-ended scientific exploration.
> **Caution!**
> This codebase will execute Large Language Model (LLM)-written code. There are various risks and challenges associated with this autonomy, including the potential use of dangerous packages, uncontrolled web access, and the possibility of spawning unintended processes. Ensure that you run this within a controlled sandbox environment (e.g., a Docker container). Use at your own discretion.
## Table of Contents
1. [Requirements](#requirements)
* [Installation](#installation)
* [Supported Models and API Keys](#supported-models-and-api-keys)
2. [Generate Research Ideas](#generate-research-ideas)
3. [Run AI Scientist-v2 Paper Generation Experiments](#run-ai-scientist-v2-paper-generation-experiments)
4. [Citing The AI Scientist-v2](#citing-the-ai-scientist-v2)
5. [Frequently Asked Questions](#frequently-asked-questions)
6. [Acknowledgement](#acknowledgement)
## Requirements
This code is designed to run on Linux with NVIDIA GPUs using CUDA and PyTorch.
### Installation
```bash
# Create a new conda environment
conda create -n ai_scientist python=3.11
conda activate ai_scientist
# Install PyTorch with CUDA support (adjust pytorch-cuda version for your setup)
conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
# Install PDF and LaTeX tools
conda install anaconda::poppler
conda install conda-forge::chktex
# Install Python package requirements
pip install -r requirements.txt
```
Installation usually takes no more than one hour.
### Supported Models and API Keys
#### OpenAI Models
By default, the system uses the `OPENAI_API_KEY` environment variable for OpenAI models.
#### Gemini Models
By default, the system uses the `GEMINI_API_KEY` environment variable for Gemini models through OpenAI API.
#### Claude Models via AWS Bedrock
To use Claude models provided by Amazon Bedrock, install the necessary additional packages:
```bash
pip install anthropic[bedrock]
```
Next, configure valid [AWS Credentials](https://docs.aws.amazon.com/cli/v1/userguide/cli-configure-envvars.html) and the target [AWS Region](https://docs.aws.amazon.com/bedrock/latest/userguide/bedrock-regions.html) by setting the foExcerpt of 13,572 characters
Read on GitHub
Cong Lu · United Kingdom
17
Takanori Kotama
17
11
Robert Tjarko Lange · Japan
5
4
OpenHands · @all-hands-ai
2
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:44cd24d1c1befe94, desc:agentic