test4

This capability allows for dynamic function calling based on a schema that defines the expected inputs and outputs. It integrates with multiple model context protocols (MCPs) to facilitate seamless communication between different AI models and services. By utilizing a registry of functions, it can route requests to the appropriate provider based on the schema, enabling flexibility in choosing models and APIs for various tasks.

This capability provides a mechanism for managing the context of interactions with AI models over time. It employs a context stack that retains relevant information across multiple calls, allowing for more coherent and contextually aware responses. This is particularly useful for applications that require maintaining user state or conversation history.

This capability orchestrates tasks across multiple AI models, allowing for complex workflows that leverage the strengths of different models. It uses a pipeline architecture where tasks can be distributed to various models based on their capabilities, enabling a modular approach to AI task execution.