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SGLang Inference Serving

Serve LLMs fast with structured JSON/regex outputs and automatic prefix caching

Source Download skill
Repository
Orchestra-Research/AI-Research-SKILLs
Stars
14,195
Licence
MIT
Files
4 files

What it does

This skill teaches the agent how to set up and use SGLang, an open-source framework for running large language models as a server. Once installed, the agent can write code that launches an SGLG server, forces model output into formats like JSON, regex patterns or grammars, and reuses cached computation for repeated prompts such as shared system prompts or tool definitions. It is a reference guide for building fast, structured LLM applications rather than a tool that does work on its own.

Great for

  • Engineers deploying open-weight LLMs who need reliable JSON or structured output
  • Teams building multi-turn or agentic apps with long, repeated system prompts and tool lists
  • Developers who already have GPUs and want a faster alternative to a standard serving setup

Skip it if

  • Users without a GPU or technical setup, since the framework is aimed at running models on server hardware
  • Simple text generation where the stated alternative (vLLM) is sufficient, because SGLang adds complexity without much benefit
  • Anyone relying on the headline speed claims (5x, 10x, 3x) as guarantees; these are unverified marketing figures from the description and depend heavily on workload

Try asking

  • “Set up an SGLang server for Llama 3 8B with prefix caching enabled and show me the launch command”
  • “Write a Python function that extracts name, age and occupation from text as strict JSON using SGLang”
  • “Help me build an agent with function calling where the system prompt and tool definitions are reused across requests”
Step-by-step guide: how to use SGLang Inference ServingInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 4 original files, the MIT licence and an attribution note.

    sglang.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip sglang.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip sglang.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Ask the agent to check your CUDA version before installing, since the flashinfer command in the guide is pinned to specific CUDA and torch versions
  • Review any generated server launch command before running it on a shared or production machine, and keep the port bound to localhost unless you intend otherwise
  • Verify performance claims by benchmarking your own workload against your current serving stack

What's inside

  • references/deployment.md
  • references/radix-attention.md
  • references/structured-generation.md
  • SKILL.md

Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.