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sglang

Serve structured LLM generations with RadixAttention prefix caching.

Updated Aug 23, 2026
Search Tags:#tool calling#json schema#structured generation#prefix caching#llm serving#radixattention#regex decoding
One-click install
npx skills add https://github.com/box755/simlens-research --skill sglang-box755
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sglang
Source: https://github.com/box755/simlens-research/tree/main/skill-packs/AI-Research-SKILLs/12-inference-serving/sglang
Command: npx skills add https://github.com/box755/simlens-research --skill sglang-box755

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

SGLang solves slow and inconsistent LLM serving when you need structured outputs (like JSON/regex/grammar) and repeated prompt prefixes across many requests, by reusing cached computation through RadixAttention.

Core Features & Use Cases

  • Fast structured generation: Produce valid JSON via constrained decoding, including regex constraints and grammar-based generation.
  • RadixAttention prefix caching: Automatically caches and reuses shared prompt prefixes (e.g., system prompts, few-shot examples, tool definitions) to reduce repeated KV computation.
  • Production serving + agent workflows: Run an OpenAI-compatible inference server and support tool/function calling patterns for agentic applications where prompts are largely repeated but queries vary.
  • Use Case: Build an agent that repeatedly uses the same system prompt and tool schema while handling many user queries; RadixAttention reuses the shared prefix so each new request computes only the new suffix.

Quick Start

Use the sglang skill to launch a local OpenAI-compatible server and generate constrained JSON answers with RadixAttention enabled.

Frequently Asked Questions about sglang

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

FAQPage Schema
How do I speed up structured LLM generation when prompts share the same prefix?▼

Speed up structured LLM generation by using RadixAttention prefix caching to automatically cache and reuse shared prompt prefixes, reducing repeated KV computation for JSON or regex-constrained outputs.

What is the best way to serve regex and JSON schema constrained outputs for production inference?▼

Serve regex and JSON schema constrained outputs efficiently using an OpenAI-compatible SGLang server, leveraging constrained decoding parameters and RadixAttention for production inference.

Does RadixAttention work with tool calling and agent workflows?▼

RadixAttention works with tool calling agent workflows by reusing shared prompt prefixes like system prompts and tool definitions, computing only the new suffix for each varying user query.

How do I set up my environment for prefix caching and structured generation?▼

Set up prefix caching and structured generation by deploying an OpenAI-compatible SGLang server with dependencies including torch and transformers, then applying regex or JSON schema decoding parameters.

Why is my LLM serving slow when generating JSON outputs from repeated few-shot examples?▼

LLM serving is slow for JSON outputs from repeated few-shot examples because of redundant KV computation, which RadixAttention prefix caching solves by automatically reusing the shared prefix tokens.

When should I not use RadixAttention for structured generation?▼

Avoid RadixAttention for structured generation when prompt prefixes are largely unique across requests, as the caching mechanism provides no KV computation reduction benefit without shared prefix workloads.

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