Streaming Json Extraction With Progressive Object Emission

via “streaming partial object construction”

Get structured, validated outputs from LLMs using Pydantic models — patches any LLM client.

Unique: Implements a token-aware JSON parser that can detect field boundaries in incomplete JSON, allowing validation of individual fields before the full response is complete. Uses a state machine to track parsing progress and yield partial objects at natural boundaries (e.g., when a field is complete).

vs others: More efficient than buffering the entire response before validation (enables real-time feedback) and more robust than naive token-by-token parsing (handles nested structures and arrays correctly)

Parse partial JSON generated by LLM

Unique: Implements an event-emitter pattern where the parser maintains internal state across token boundaries and fires 'data' events only when complete JSON objects/arrays are detected, enabling true streaming consumption without buffering the entire response

vs others: More efficient than line-by-line or chunk-based parsing because it respects JSON structure rather than arbitrary delimiters, and more responsive than waiting for full completion because it emits results as soon as closing brackets appear