Tributary

Leverages asynchronous Python generators for synchronous, event-driven data streams, enabling complex event processing as shown in the streaming examples.

Uses directed acyclic graphs to propagate changes only when inputs update, optimizing performance for computationally expensive models like options pricing.

Supports gradient computation on both lazy and streaming graphs, facilitating machine learning and optimization tasks without external libraries.

Integrates with Graphviz and ipydagred3 for live monitoring and debugging, as demonstrated in the animated GIFs for streaming and lazy graphs.