Serve LLMs with RadixAttention prefix caching and structured JSON/regex outputs.
npx skills add https://github.com/arsity/scholar-tools --skill sglang-arsity
Please help me install this Agent Skill. Skill: sglang Source: https://github.com/arsity/scholar-tools/tree/main/vendor/ai-research-skills/12-inference-serving/sglang Command: npx skills add https://github.com/arsity/scholar-tools --skill sglang-arsity
SGLang dramatically speeds up structured generation and agent workflows by automatically caching shared prefixes in prompts (RadixAttention), enabling reliable, high-throughput JSON/regex/grammar outputs for production-grade LLM serving.
Start a server that serves an OpenAI-compatible API and returns structured outputs for tool-assisted workflows.
High-intent search queries and answers about installing and using this skill.
RadixAttention prefix caching accelerates LLM serving by automatically caching shared prompt prefixes, such as system prompts and few-shot examples, to dramatically speed up repeated multi-turn agent workflows and conversations.
Yes, you can use an OpenAI-compatible API for multi-model hosting with structured generation, utilizing SGLang's production deployment features like batching and metrics to achieve scalable inference for real-world applications.
Yes, you need Python and PyTorch to set up structured LLM serving, as SGLang requires a Python environment with dependencies including torch and transformers to automate fast, structured generation and RadixAttention prefix caching.
The best way to speed up repeated system prompts in agent workflows is using RadixAttention prefix caching, which automatically caches shared prompt prefixes to enable high-throughput and reliable structured LLM serving.
Structured generation with regex constraints can fail in high-throughput LLM serving when shared prefixes are not cached, causing latency; SGLang solves this by automatically applying RadixAttention to maintain deterministic outputs at scale.
kaminocorpDeploy vLLM serving infrastructure with OpenAI-compatible endpoints and quantization.
EndUser123Streams Llama, Mixtral, Gemma, and DeepSeek inference via Groq's OpenAI-compatible endpoint.
tadod12Serve LLMs with structured JSON and regex outputs using RadixAttention prefix caching.
Supporter09Generate structured, prefix-cached LLM outputs for serving workloads.
