Fast structured generation and serving for LLMs with RadixAttention prefix caching.
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.This is the skill's description: the text your AI reads to decide when the skill applies.
orchestra-sglang/SKILL.md
---
name: sglang
description: Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Inference Serving, SGLang, Structured Generation, RadixAttention, Prefix Caching, Constrained Decoding, Agents, JSON Output, Fast Inference, Production Scale]
dependencies: [sglang, torch, transformers]
---
# 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… load the full skill through Skill Me