sglang
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
Install
npx skills add davila7/claude-code-templates@sglangRuns in your terminal. Adds the skill globally for Claude Code, Cursor, Codex and others; add -g -y to skip prompts.
What it does
SGLang High-performance serving framework for LLMs and VLMs with RadixAttention for automatic prefix caching. When to use SGLang Use SGLang when: - Need structured outputs (JSON, regex, grammar) - Building agents with repeated prefixes (system prompts, tools) - Agentic workflows with function calling - Multi-turn conversations with shared context - Need faster JSON decoding (3× vs standard) Use vLLM instead when: - Simple text generation without structure - Don't need prefix caching - Want mature, widely-tested production system Use TensorRT-LLM instead when: - Maximum single-request latency…
Excerpt from the skill's own SKILL.md. Read the full file on GitHub before installing: skills run with your agent's permissions.
View source on GitHubBefore you install
Skills are plain text instructions the agent follows, sometimes with scripts. Check the source, prefer repositories with many installs and stars, and read any script it ships.
Categories
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