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This capability enables users to define and call functions using a schema-based approach, allowing for seamless integration with multiple model providers such as OpenAI and Anthropic. It utilizes a registry of function definitions that can be dynamically resolved at runtime, ensuring flexibility and extensibility in function invocation. The architecture supports both synchronous and asynchronous calls, optimizing for performance based on the provider's capabilities.

This capability manages the contextual state across multiple interactions with AI models, ensuring that relevant context is preserved and utilized in subsequent calls. It employs a context stack that captures user interactions and model responses, allowing for a rich conversational experience. The architecture is designed to minimize context loss by intelligently managing the state lifecycle based on user-defined rules.

This capability orchestrates multiple API calls in real-time to create complex AI workflows, allowing users to define sequences of operations that can include data processing, model inference, and result aggregation. It uses a visual workflow editor that enables users to design and modify workflows without deep programming knowledge. The orchestration engine is built to handle asynchronous operations efficiently, ensuring smooth execution of workflows.