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.

Captum is an extensible library that provides general-purpose implementations for model interpretability in PyTorch. It aims to demystify complex machine learning models by offering several algorithms to analyze and understand model predictions. Captum includes a variety of methods such as feature ablation, integrated gradients, and others, which help researchers and developers to comprehend and improve their models.

Teammately is the autonomous AI agent designed for AI engineers to build, evaluate, and refine AI products, models, and agents. It empowers you to define your objectives, and then autonomously iterates using LLMs, prompts, RAG, and ML to achieve results beyond human-level manual iteration. Teammately focuses on a scientific approach to AI development, ensuring quality and reliability through AI-driven testing and evaluation.

Algomax is an innovative platform that focuses on optimizing LLM and RAG model output evaluation. It simplifies complex prompting development and offers insights into qualitative metrics. The platform is designed to enhance productivity by providing a seamless and efficient workflow for evaluating and improving model outputs. This holistic approach ensures that users can quickly and effectively iterate on their models and prompts, resulting in higher-quality outputs in less time.

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.