sglang

You can serve structured LLM outputs faster by using RadixAttention-based prefix caching to reuse shared context across repeated prompts, avoiding full-prompt recomputation and improving throughput for multi-turn chat and agent workflows.

Generating JSON schema-constrained outputs is best handled through constrained decoding during inference, ensuring valid structured generation without brittle post-processing for extraction pipelines and API backends.

Prefix caching works with tool-calling and multi-turn chat workloads by leveraging RadixAttention to cache and reuse repeated system prompts and shared context across requests, significantly improving serving throughput.

You can use regex and grammar constraints for structured generation alongside JSON schema formats, enabling strict output control for multimodal inference and extraction scenarios within an OpenAI-compatible serving environment.

LLM serving slows down with repeated system prompts because of full-prompt recomputation, which prefix caching with RadixAttention solves by reusing shared context across requests to improve overall throughput.

You need an OpenAI-compatible serving environment to utilize constrained decoding and RadixAttention-based prefix caching, ensuring structured outputs and efficient context reuse for your LLM serving workloads.