sglang

Serve LLMs and VLMs with RadixAttention prefix caching and structured generation.

2|Updated Feb 4, 2026

Search Tags:#inference #agentic workflows #structured generation #llm serving #radixattention #fast inference

Ready to run

npx skills add https://github.com/zhuangbiaowei/smart_bot --skill sglang-zhuangbiaowei

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sglang
Source: https://github.com/zhuangbiaowei/smart_bot/tree/main/skills/sglang
Command: npx skills add https://github.com/zhuangbiaowei/smart_bot --skill sglang-zhuangbiaowei

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a high-performance serving framework for Large Language Models (LLMs) and Vision-Language Models (VLMs), significantly accelerating inference speed through its innovative RadixAttention mechanism.

Core Features & Use Cases

Fast Inference: Achieves up to 5x faster inference compared to standard vLLM, especially for agentic workflows and few-shot prompting, due to automatic prefix caching.
Structured Generation: Excels at generating structured outputs like JSON and adhering to regex or grammar constraints, making it ideal for agentic workflows with tool calls.
Use Case: Building an AI agent that frequently uses the same system prompt and tool definitions. SGLang's RadixAttention will cache these common prefixes, drastically reducing the time and computational cost for each subsequent agent turn.

Quick Start

Launch the SGLang server with the Llama 3-8B Instruct model on port 30000.

Frequently Asked Questions about sglang

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

FAQPage Schema

How do I speed up LLM serving for agentic workflows with repetitive prompt prefixes?▼

To speed up LLM serving for agentic workflows, you can use a framework with RadixAttention for automatic prefix caching. This mechanism caches common system prompts and tool definitions, drastically reducing time and computational cost for subsequent turns.

What is the best way to enforce structured generation and JSON constraints during LLM inference?▼

The best way to enforce structured generation during LLM inference is using a serving framework that natively supports JSON outputs and regex constraints. This ensures the model strictly adheres to specific grammar rules for tool calls.

How does RadixAttention work for automatic prefix caching in LLM serving?▼

RadixAttention works by using a radix tree to manage the KV cache memory during LLM serving. It automatically identifies and reuses shared prompt prefixes across requests, enabling up to 5x faster inference compared to standard vLLM.

Can I launch an LLM server for structured generation using Llama 3 models?▼

Yes, you can launch an LLM server for structured generation using models like Llama 3-8B Instruct. The server specializes in handling structured outputs and agentic workflows, and can be started on a specific port.

How does fast inference with RadixAttention compare to standard vLLM serving?▼

Fast inference with RadixAttention provides up to 5x speedups compared to standard vLLM serving. The performance gain is especially significant in few-shot prompting and agentic workflows due to automatic prefix caching.

When do I need automatic prefix caching for LLM inference?▼

You need automatic prefix caching for LLM inference when building AI agents that frequently reuse the same system prompts and tool definitions. It accelerates inference by avoiding the redundant computation of identical prompt prefixes.