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The Cradle framework is a first attempt at General Computer Control (GCC). Cradle supports agents to ace any computer task by enabling strong reasoning abilities, self-improvment, and skill curation, in a standardized general environment with minimal requirements.
The main features of baai-agents/cradle are: Agent Memory Systems, AI Agents.
Projects with overlapping indexed features include: simular-ai/agent-s — Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through… os-copilot/os-copilot — [[Website]](https://os-copilot.github.io/) [[Arxiv]](https://arxiv.org/abs/2402.07456)… mnemox-ai/tradememory-protocol — The Tradememory Protocol is a persistent, multi-layered memory and audit framework designed for artificial… wassupjay/n8n-free-templates — This project is a library of pre-configured n8n workflow templates and structural blueprints designed for automating… sahat/hackathon-starter — This project is a Node.js web application boilerplate designed to accelerate development by providing a pre-configured… airi-institute/arigraph — AriGraph functions as the external memory architecture for large language models (LLMs), featuring a knowledge graph…
[Website](https://os-copilot.github.io/) [Arxiv](https://arxiv.org/abs/2402.07456) [PDF](https://arxiv.org/pdf/2402.07456.pdf) [Documentation](https://os-copilot.readthedocs.io/en/latest/)
Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through graphical user interface interactions. It functions as a computer use interface, utilizing vision-language grounding to translate natural language goals into precise screen coordinates and system actions. The project differentiates itself by combining structured accessibility tree inspection with vision-based element localization. It manages cross-application workflows by mapping conceptual descriptions to physical pixels and simulating low-level keyboard and mouse events to mov
The Tradememory Protocol is a persistent, multi-layered memory and audit framework designed for artificial intelligence trading agents. It provides a structured architecture for agents to maintain episodic, semantic, and procedural knowledge across trading sessions, ensuring that decision-making is informed by long-term recall and historical context. The framework distinguishes itself through a combination of cryptographic integrity and cognitive modeling. It employs a tamper-evident logging system that uses hashed sequences to verify the history of trading decisions, alongside an outcome-wei
This project is a Node.js web application boilerplate designed to accelerate development by providing a pre-configured foundation with integrated routing, templating, and developer tooling. It serves as a comprehensive starter kit that includes a full-stack authentication system, a payment integration starter, and an LLM agent framework. The framework distinguishes itself with specialized tools for AI development, including a retrieval-augmented generation implementation kit with vector search and semantic caching. It enables the creation of reasoning agents featuring tool-calling loops and r