sglang

Automate prompt prefix caching for LLM serving with SGLang.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/t2ance/dr-claw-plugin --skill sglang-t2ance
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: sglang
Source: https://github.com/t2ance/dr-claw-plugin/tree/main/plugins/ml-training-stack/skills/inference-serving/sglang
Command: npx skills add https://github.com/t2ance/dr-claw-plugin --skill sglang-t2ance

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Automates prompt prefix caching to accelerate production-grade LLM serving by reusing shared prompt prefixes across requests.

Core Features & Use Cases

  • RadixAttention-based automatic KV cache reuse for system prompts, few-shot prompts, and multi-turn conversations.
  • Structured generation support (JSON, regex, grammars) and agent workflows with function calls.
  • Production-ready API compatibility (OpenAI-compatible) with multi-model and multi-GPU deployment coverage.

Quick Start

Install SGLang and launch the server with a model path to begin serving structured generation at scale.

Frequently Asked Questions about sglang

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

FAQPage Schema
How does RadixAttention speed up prompt prefix caching for LLM serving?

RadixAttention accelerates LLM serving by automatically reusing KV cache from shared system prompts, few-shot examples, and multi-turn conversations across multiple requests to skip redundant processing.

How do I get structured JSON output from an LLM serving endpoint?

You can get structured JSON output by launching the server with a compatible model path and utilizing its built-in structured generation support, which enforces JSON, regex, or grammar constraints on the generated output.

Can I deploy LLM agents with function calling using an OpenAI-compatible API?

Yes, this solution supports building agent workflows with function calls and provides a production-ready OpenAI-compatible API to serve them. It handles multi-model and multi-GPU deployments out of the box.

Do I need a specific Python environment to run structured generation at scale?

Yes, you need a Python environment with the sglang package installed and a compatible model path available locally to launch the server and begin serving structured generation requests.

What is the best way to handle multi-turn conversations without slowing down LLM inference?

The best approach is using a server with automatic KV cache reuse for multi-turn conversations. This prevents the system from recalculating the shared prefix tokens for each new turn, maintaining fast inference speeds.