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sglang

Serve LLMs with prefix caching and constrained JSON, regex, or grammar outputs.

2|Updated Apr 12, 2026
Search Tags:#agents#json#structured-generation#inference-serving#sglang#prefix-caching#radixattention
One-click install
npx skills add https://github.com/Clay-HHK/claude-config --skill sglang-clay-hhk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sglang
Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/AI-research-SKILLs/12-inference-serving/sglang
Command: npx skills add https://github.com/Clay-HHK/claude-config --skill sglang-clay-hhk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

SGLang eliminates wasted compute and slow, error-prone outputs when serving large language models by automatically caching repeated prefixes and enabling constrained, structured decoding so production systems get valid JSON/regex/grammar outputs at high throughput and low latency.

Core Features & Use Cases

  • Automatic RadixAttention prefix caching to reuse KV caches across requests and dramatically reduce repeated computation for system prompts, few-shot examples, and multi-turn histories.
  • Structured generation with regex, JSON schema, and grammar constraints for reliable machine-readable outputs and robust agent tool/function calling.
  • Production-ready serving: multi-GPU tensor parallelism, speculative decoding, OpenAI-compatible API endpoint, autoscaling guides, and Kubernetes/Docker deployment patterns.
  • Use cases include high-throughput agent backends with repeated system prompts, few-shot retrieval pipelines, form or API-response generation with strict schemas, and multi-modal inference.

Quick Start

Launch a local SGLang server pointing to your model path and query its OpenAI-compatible endpoint to begin fast, prefix-cached structured generation.

Frequently Asked Questions about sglang

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

FAQPage Schema
How do I serve structured LLM outputs with strict JSON schemas?▼

Serve structured LLM outputs by applying constrained decoding with JSON schema, regex, and grammar constraints during generation. This ensures valid machine-readable responses and robust agent tool calling without error-prone post-processing.

How does prefix caching reduce LLM inference latency for repeated system prompts?▼

Prefix caching uses RadixAttention to automatically reuse KV caches across requests with repeated system prompts, few-shot examples, and multi-turn histories. This eliminates redundant computation, dramatically reducing latency and wasted compute.

Can I deploy multi-GPU tensor parallelism for production LLM serving?▼

Yes, production LLM serving supports multi-GPU tensor parallelism, speculative decoding, and OpenAI-compatible API endpoints. It includes Kubernetes and Docker deployment patterns with autoscaling guides for high-throughput multi-GPU environments.

What is the best way to speed up few-shot retrieval pipelines and agent backends?▼

Speed up few-shot retrieval pipelines and agent backends by combining automatic RadixAttention prefix caching with constrained structured generation. This reuses KV caches for repeated prompts while ensuring valid JSON outputs, maximizing throughput and minimizing latency.

Does structured generation work with an OpenAI-compatible API endpoint?▼

Yes, structured generation works with an OpenAI-compatible API endpoint. Launch a local server pointing to your model path and query the endpoint to receive fast, prefix-cached structured outputs constrained by JSON schemas, regex, or grammars.

Why does my LLM serving produce invalid JSON for agent tool calling?▼

LLM serving produces invalid JSON for agent tool calling without constrained decoding. Applying structured generation with JSON schema and grammar constraints during inference guarantees valid machine-readable outputs and robust function calling at high throughput.

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