sglang

Serve structured JSON and regex outputs for LLMs with RadixAttention.

6|3|Updated Jan 29, 2026

Search Tags:#LLM #RadixAttention #structured generation #prefix caching #fast inference

Ready to run

npx skills add https://github.com/jonnabio/ace-framework --skill sglang-jonnabio

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sglang
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/sglang
Command: npx skills add https://github.com/jonnabio/ace-framework --skill sglang-jonnabio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sglang, torch, transformers, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill enables rapid, structured generation and serving for Large Language Models (LLMs) with RadixAttention, improving performance by 5-10x in agent workloads.

Core Features & Use Cases

Structured Generation: Supports JSON, regex, and grammar-based outputs.
RadixAttention: Automatically caches and reuses common prefixes across requests.
Use Case: For a multi-turn conversation, this Skill can quickly generate a structured JSON output with a high throughput, leveraging the RadixAttention feature for significant performance gains.

Quick Start

Use the sglang skill to generate a structured JSON output for the provided text with the specified schema.

Frequently Asked Questions about sglang

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How does RadixAttention improve LLM inference performance?▼

RadixAttention improves LLM inference by automatically caching and reusing common prefixes across requests, yielding 5-10x faster performance in agent workloads.

How do I generate JSON output from an LLM with constrained decoding?▼

Generate structured JSON output by applying constrained decoding with a specified schema, utilizing RadixAttention for high-throughput LLM serving.

Can I use this approach for multi-turn conversations requiring regex outputs?▼

Yes, it supports multi-turn conversations by rapidly generating regex or grammar-based structured outputs while leveraging prefix caching for performance gains.

Do I need PyTorch and transformers to run sglang for structured generation?▼

Yes, you need PyTorch, transformers, and sglang installed as dependencies to execute this structured generation and serving workflow.

What is the best way to achieve high-throughput LLM serving for agent workflows?▼

Using RadixAttention for structured output serving is highly effective for high-throughput, agent-driven workflows, automatically caching common prefixes to boost performance.