🧩 AI FrameworksOpen SourceFree

SGLang

High-performance LLM serving with structured output — JSON mode, regex constraints

4.5/ 5⭐ 15,000 GitHub starsPythonApache-2.0

📋 Overview

SGLang is a high-performance serving framework for large language models and multimodal models — with built-in structured output support (JSON mode, regex constraints, JSON schema). Unlike vLLM (which focuses on raw throughput) or TensorRT-LLM (which requires complex compilation), SGLang provides a RadixAttention-based prefix caching system that dramatically accelerates multi-turn conversations and few-shot prompts. The structured output compiler guarantees valid JSON or regex-constrained outputs at near-native speed, making it the default for production APIs that need reliable structured responses.

✨ Key Features

•30K+ GitHub stars — high-performance LLM serving
•RadixAttention: prefix caching for multi-turn conversations
•Structured output compiler: JSON mode, regex, JSON schema
•Near-native speed for constrained decoding
•Tensor parallelism and continuous batching
•OpenAI-compatible API
•Multimodal support: LLaVA, Qwen-VL

🎯 The Problem It Solves

Production LLM serving requires both high throughput and structured outputs (valid JSON, schema-constrained). Existing engines either optimize for speed (vLLM) or structure (outlines), but not both. SGLang delivers both with RadixAttention caching and a compiled constrained-decoding backend.

🔧 How It Works

SGLang uses RadixAttention — a prefix caching system that stores KV cache for common prompt prefixes in a radix tree, enabling automatic reuse across requests. For structured outputs, it compiles regex/JSON schemas into finite-state machines that guide token generation at near-native speed. The framework supports tensor parallelism, continuous batching, and OpenAI-compatible API. It runs Llama, Mistral, Gemma, Qwen, and multimodal models (LLaVA, Qwen-VL) with minimal configuration.

🚀 Installation & Quick Start

Installation

See website

Quick Start

See documentation

✅ Pros

•Best-in-class structured output generation
•RadixAttention caching accelerates multi-turn conversations
•Near-native speed for constrained decoding
•OpenAI-compatible API
•Multimodal model support
•Apache 2.0 licensed — completely free
•Active development from UC Berkeley

❌ Cons

•Smaller community than vLLM
•Newer framework — occasional bugs
•Structured output compiler adds startup overhead
•Documentation lags behind features
•Limited model support compared to vLLM

💬 Practitioner Verdict

“SGLang is the best choice for production LLM serving with structured outputs — the RadixAttention caching is genuinely innovative, and the JSON mode is faster than alternatives. The trade-off: smaller community than vLLM, newer framework with occasional bugs, and the structured output compiler adds startup overhead. For APIs that need reliable JSON responses at scale, SGLang is the default.”