sglang

Cache and reuse shared KV prefixes to accelerate LLM generation.

Updated Apr 3, 2026

Search Tags:#openai-api #structured-generation #agent-workflows #sglang #llm-serving #prefix-caching #radixattention

Ready to run

npx skills add https://github.com/handsomelong922/my-codex-skills --skill sglang-handsomelong922

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sglang
Source: https://github.com/handsomelong922/my-codex-skills/tree/main/skills/sglang
Command: npx skills add https://github.com/handsomelong922/my-codex-skills --skill sglang-handsomelong922

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

RadixAttention-based LLM serving accelerates generation by caching and reusing common prefixes, reducing compute and latency across repeated prompts.

Core Features & Use Cases

Automatic prefix caching across system prompts, few-shot examples, and multi-turn conversations.
Production-ready OpenAI-compatible API with scalable server deployment options.
Supports agent workflows, tool calls, and structured generation patterns at scale.

Quick Start

Start the SGLang server with your model path, then connect a client to the /v1/chat/completions endpoint to begin interacting with the accelerated model.

Frequently Asked Questions about sglang

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

FAQPage Schema

How do I accelerate LLM serving for repeated system prompts in multi-turn conversations?▼

LLM serving for repeated system prompts is accelerated by RadixAttention, an automatic prefix caching mechanism that reuses common KV prefixes across requests to reduce compute and latency. SGLang implements this to optimize multi-turn agent workflows and few-shot prompts.

Can I deploy an OpenAI-compatible API for agent workflows with structured generation?▼

An OpenAI-compatible API endpoint is fully supported for agent workflows and structured generation at scale. You start the SGLang server with your model path and connect a client to the /v1/chat/completions endpoint to begin interacting with the accelerated model.

What is RadixAttention and when do I need it for prefix caching?▼

RadixAttention is a prefix caching mechanism that automatically caches and reuses KV prefixes from shared system prompts, few-shot examples, and multi-turn conversations. You need it when deploying LLMs where the same prompts and tools are reused across requests to accelerate generation and reduce compute.

Does SGLang support tool calls and few-shot prompts at production scale?▼

Tool calls, few-shot prompts, and structured generation patterns are supported at production scale. The server automatically caches shared prefixes across these patterns to reduce compute and latency across repeated requests during scalable deployment.

Why does prefix caching not work for my unique prompts in LLM serving?▼

Prefix caching accelerates generation by reusing common prefixes across repeated requests, so unique prompts with no shared system prompts or few-shot examples will not benefit from the RadixAttention cache and will experience standard serving latency.

What is the best way to serve LLMs with shared prefixes across multiple requests?▼

The best way to serve LLMs with shared prefixes is using a RadixAttention-based server that automatically caches KV prefixes. SGLang provides this functionality with a scalable, production-ready OpenAI-compatible API to accelerate generation and reduce compute.