sglang

Speed up LLM inference for multi-turn conversations by using RadixAttention automatic prefix caching, which stores and reuses shared prefixes to deliver dramatic throughput gains in agent workflows.

Enforce structured JSON output when serving LLMs by applying constrained decoding patterns like regex and grammars through an OpenAI-compatible API, ensuring deterministic and validated results for production environments.

Scale LLM serving for tool calls and repeated prefixes by utilizing automatic prefix caching and production-ready deployment tooling, which optimizes throughput and reduces latency for repeated context generation.

Yes, you can use an OpenAI-compatible API for structured generation by applying regex and grammar constraints, enabling deterministic outputs that integrate seamlessly with existing OpenAI API client workflows.

RadixAttention prefix caching significantly improves performance for constrained decoding tasks by automatically caching shared conversation prefixes, minimizing redundant processing and accelerating deterministic JSON or regex outputs.

LLM serving latency spikes during multi-turn agent workflows due to redundant prefix processing, which can be resolved by using RadixAttention caching to automatically store and reuse shared prefixes for faster inference.