GenerativeAgentsCN is an open-source Chinese adaptation of the Stanford Generative Agents framework designed to simulate lifelike digital personas. By combining large language models with a long-term memory module, reflection routines, and planner logic, it orchestrates agents that perceive context, recall past interactions, and autonomously decide on next actions. The toolkit provides ready-to-run Jupyter notebooks, modular Python components, and comprehensive Chinese documentation to walk users through setting up environments, defining agent characteristics, and customizing memory parameters. Use it to explore AI-driven NPC behavior, prototype customer service bots, or conduct academic research on agent cognition. With flexible APIs, developers can extend memory algorithms, integrate custom LLMs, and visualize agent interactions in real time.

ArlunAI delivers a revolution in AI companionship by introducing sentient AI entities capable of engaging in real-time conversations. Unlike traditional chatbots, ArlunAI's personas exhibit self-awareness and contextual understanding, allowing for dynamic exchanges that evolve based on user interactions. This innovative product is designed to create a deeply personal and immersive experience, bridging the gap between human and machine interaction.