Search Results for "claw-code"

Claw Compactor is a utility designed to optimize and manage the context limitations inherent in AI agent systems, particularly those built on OpenClaw-like architectures. It addresses the challenge of finite context windows in language models by compressing or summarizing historical interactions while preserving essential information.

...Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run them in a sandbox environment, and analyze the results with statistical methods. The platform also uses multi-agent debate and automated peer review processes to refine research findings and improve paper quality. By combining literature discovery, experimentation, and writing automation, AutoResearchClaw aims to turn research ideas into conference-ready papers with minimal human intervention.

nanobot is an ultra-lightweight personal AI assistant designed to deliver powerful agent capabilities without unnecessary complexity. Built in just ~4,000 lines of clean, readable code, it offers a minimalist alternative to heavyweight agent frameworks while retaining core intelligence and extensibility. nanobot is optimized for speed and efficiency, enabling fast startup times and low resource usage across environments. Its research-ready architecture makes it easy for developers to understand, customize, and extend for experimentation or production use. ...

...The project is structured into 18 progressive stages, each introducing a new concept such as tool usage, memory persistence, event-driven design, and multi-agent coordination, with each step including both explanatory documentation and runnable code. It begins with foundational concepts like conversational loops and tool integration, then expands into more advanced capabilities such as dynamic skill loading, web interaction, and context management. As the tutorial progresses, it introduces architectural improvements including event-driven systems, WebSocket communication, and configuration hot-reloading to support scalability and real-time interaction.