SGLang High-performance serving framework for LLMs and VLMs with RadixAttention for automatic prefix caching. When to use SGLang Use SGLang when: - Need structured outputs (JSON, regex, grammar) - Building agents with repeated prefixes (system prompts, tools) - Agentic workflows with function calling - Multi-turn conversations with shared context - Need faster JSON decoding (3× vs standard) Use vLLM instead when: - Simple text generation without structure - Don't need prefix caching - Want mature, widely-tested production system Use TensorRT-LLM instead when: - Maximum single-request latency…