The AI Agent Debate Autogen Tutorial provides a step-by-step framework for orchestrating multiple AI agents engaged in structured debates. It leverages LangChain’s AutoGen module to coordinate messaging, tool execution, and debate resolution. Users can customize templates, configure debate parameters, and view detailed logs and summaries of each round. Ideal for researchers evaluating model opinions or educators demonstrating AI collaboration, this tutorial delivers reusable code components for end-to-end debate orchestration in Python.

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.