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This capability allows seamless integration with multiple AI models through a unified context protocol, enabling users to switch between different model providers without changing the underlying architecture. It employs a modular design that abstracts the model-specific details, allowing for easy updates and maintenance. The use of a context management layer ensures that the state is preserved across different model calls, enhancing the user experience.

This capability ensures that the context is preserved dynamically across multiple interactions with different models. It uses a stateful architecture to maintain context information, which is crucial for applications that require continuity in user interactions. The implementation leverages a context store that can be queried and updated in real-time, making it suitable for complex workflows.

This capability orchestrates API calls to various AI models, allowing users to define workflows that involve multiple models in a single request. It uses a declarative approach to define the sequence of calls and manage dependencies between them. The orchestration engine ensures that each model is called with the appropriate context and handles any errors that may arise during the process.