effect-ai-streaming

To handle AI streaming responses safely in Effect, you normalize start, delta, and end StreamPart sequences into incrementally accumulated outputs using Stream.mapChunksEffect, ensuring side-effectful accumulation without corrupting shared state.

Keeping prompt history consistent during concurrent AI chat sessions requires using SubscriptionRef to create incremental history checkpoints, combined with Prompt.concat to efficiently merge streamed parts without reprocessing the entire history on each chunk.

Preventing overlapping stream runs in an Effect AI application is achieved by applying a Semaphore combined with Channel.acquireUseRelease, which enforces resource safety and concurrency control throughout the acquire, use, and release lifecycle.

Effect AI stream dispatch fails when using incorrect tag-based matching for StreamPart objects; you must use Match.when with explicit type checks like Match.when({ type: ... }) to correctly route text, reasoning, and tool-parameter parts.

Yes, you can stream tool parameters and reasoning parts alongside text in an Effect chat UI by normalizing the stream protocol lifecycle, allowing deterministic consumption strategies like runForEach or fold to process diverse part types predictably.