OpenLens AI provides a multimodal agent for conducting scientific research.
Open source
24 alternativesAI Scientist
The AI Scientist is a research project for automating open-ended scientific discovery.
About AI Scientist
The AI Scientist is a research project from SakanaAI, hosted on GitHub, that aims to automate open-ended scientific discovery. It is described as a system that lets foundation models, such as large language models, carry out research independently. The project includes three templates, covering NanoGPT, 2D Diffusion and Grokking, which the system uses to generate ideas and run experiments. The project also links to example papers generated by the system and provides an LLM-generated paper review feature.
The codebase executes LLM-written code, which the project describes as carrying risks, including potentially dangerous packages, web access and process spawning. The project advises running it in a container with web access restricted. The code is designed for Linux with NVIDIA GPUs using CUDA and PyTorch. CPU-only machines would likely take an infeasible amount of time, and other operating systems may need significant adjustments. Installation requires texlive-full, which can take a long time. Templates other than the three provided are community contributions that the project does not maintain.
Key features
- Generates research ideas and runs experiments in NanoGPT, 2D Diffusion and Grokking templates
- Produces papers from its experiment runs, with example generated papers published
- Provides LLM-generated paper reviews
- Supports open-weight and API-only language models
- Supports OpenAI models such as GPT-4o, GPT-4o-mini and o1 via API key
- Lets users create their own templates
Best for: Researchers and machine learning engineers who want to experiment with automated AI-driven research on Linux systems with NVIDIA GPUs.
Good for
- Provides all runs and data from the associated paper for its three templates
- Includes a documented process for creating new templates
- Supports a range of open-weight and API-only models
Watch out for
- Executes LLM-written code, which the project says carries risks such as web access and dangerous packages
- Designed for Linux with NVIDIA GPUs; CPU-only machines would likely be infeasible and other operating systems may need adjustments
- Community-contributed templates beyond the three provided are not maintained by the project
AI Scientist at a glance
- Category
- AI & Machine Learning › AI Search & Research
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
AI Scientist alternatives
Tools and sites you could use instead, ranked by how closely they match.
Undermind searches scientific literature, reads papers and figures, and builds a research library with citations.
PubMed.ai searches biomedical literature, summarizes research, and creates structured reports from scientific sources.
SciPub+ combines research assistants, an academy, and publishing resources for structuring scientific knowledge.
Ai Palette offers an AI platform for product discovery and launch planning, with foresight and product-suite resources.
GPT Researcher conducts deep research across data sources using large language models.
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AI Scientist FAQ
What hardware and operating system does it need?
The code is designed to run on Linux with NVIDIA GPUs using CUDA and PyTorch. Other GPU architectures may be possible by following PyTorch guidelines. The current templates would likely take an infeasible amount of time on CPU-only machines, and other operating systems may require significant adjustments.
Which language models can it use?
The project says it supports a wide variety of models, including open-weight and API-only models. It recommends using frontier models above the capability of the original GPT-4. For OpenAI models, it uses the OPENAI_API_KEY environment variable by default.
Is it safe to run?
The project warns that the codebase executes LLM-written code with associated risks, including potentially dangerous packages, web access and process spawning. It recommends containerizing the code and restricting web access appropriately.
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