Parts-Based API Design Offers a Unified Way to Handle Multimodal AI Sessions

Multimodal AI APIs are typically documented one modality at a time, leaving developers to figure out how to manage text, images, audio, and video within a single coherent session. A parts-based approach — where each request is an ordered list of typed elements — simplifies interleaving multiple media types without requiring separate correlated API calls. Developers should account for provider-specific payload size limits, as exceeding them often returns an unhelpful error rather than a clear prompt to upload assets by URI instead. Response handling also requires a state machine approach, since text streams token by token while images arrive whole and audio may come in chunks, making uniform treatment of stream events a source of subtle bugs. Multi-turn sessions with images can grow costly quickly, and strategies like summarising older turns or letting users pin specific assets in context help keep token usage and expenses manageable.