LLM Functions provides a simple framework to bridge large language model responses with real code execution. You define functions via JSON schemas, register them with the library, and the LLM will return structured function calls when appropriate. The library parses those responses, validates the parameters, and invokes the correct handler. It supports synchronous and asynchronous callbacks, custom error handling, and plugin extensions, making it ideal for applications that require dynamic data lookup, external API calls, or complex business logic within AI-driven conversations.

UIM Protocol defines a standardized JSON schema through which AI agents can describe user interface elements, behaviors and events. It covers components such as buttons, input fields, forms, tables, trees and charts, and supports event hooks for user interactions. Frontend renderers consume UIM messages to build and update interfaces on the fly without manual UI coding. Versioned message envelopes ensure backward compatibility. By leveraging UIM Protocol, teams can iterate on conversational assistants and data dashboards faster, maintain consistent UX patterns across channels, and decouple AI decision logic from presentation layers.