ARC-AGI-2

AgentBench measures how well LLMs act autonomously across eight interactive environments, including database and operating-system tasks. The current repository carries a function-calling version integrated with AgentRL, while the older benchmark remains available as v0.1. Running the suite requires Docker, and Python 3.9 is recommended because several scientific dependencies are pinned to older versions.

SUPER evaluates whether LLM agents can configure environments, execute code, and answer questions drawn from real machine-learning and NLP research repositories. Its dataset is divided into Expert, Masked, and AutoGen task sets, with code for running agents and scoring results. Local mode executes repository code directly on the machine, while Docker and Modal backends offer safer isolation and concurrent benchmark runs.

gophernotes adds a Go kernel to Jupyter Notebook and nteract, allowing live Go code to sit alongside equations, visualizations, and explanatory text in shareable documents. It evaluates code through the gomacro interpreter. On Windows, gomacro cannot use the required plugin support for third-party packages, so those notebooks should be run through the documented Docker setup.

webclaw provides a local-first way to scrape, crawl, map, and extract websites for agents and RAG systems. Its core CLI and MCP workflow can run without an account for many pages, while a hosted API is available for bot-protected sites, JavaScript rendering, asynchronous crawl jobs, search, and production monitoring.