ChatStreamAiAgent provides developers with a lightweight Python toolkit to implement AI chat agents that stream token outputs as they are generated. It supports multiple LLM providers, asynchronous event hooks, and easy integration into web or console applications. With built-in context management and prompt templating, teams can rapidly prototype conversational assistants, customer support bots, or interactive tutorials while delivering low-latency, real-time responses.

Semantic Kernel Copilot Demo is an end-to-end reference application illustrating how to build advanced AI agents with Microsoft’s Semantic Kernel framework. The demo features prompt chaining for multi-step reasoning, memory management to recall context across sessions, and a plugin-based skill architecture enabling integration with external APIs or services. Developers can configure connectors for Azure OpenAI or OpenAI models, define custom prompt templates, and implement domain-specific skills such as calendar access, file operations, or data retrieval. The sample shows how to orchestrate these components to create a conversational Copilot capable of understanding user intents, executing tasks, and maintaining context over time, fostering rapid development of personalized AI assistants.