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.

Anti-Agent-Agent provides a programmable framework to generate both adversarial and defensive AI agents for conversational models. It automates prompt crafting, scenario simulation, and vulnerability scanning, producing detailed security reports and metrics. The toolkit supports integration with popular LLM providers like OpenAI and local model runtimes. Developers can define custom prompt templates, control agent roles, and schedule periodic tests. The framework logs each interaction, highlights potential weaknesses, and recommends remediation steps to strengthen AI agent defenses, offering an end-to-end solution for adversarial testing and resilience evaluation in chatbot and virtual assistant deployments.