SGLang

SGLang is a high-performance open-source serving framework for large language and multimodal models, with 37,000+ stars on GitHub, optimized for agentic workloads and large-scale deployment.

Overview

SGLang, from the LMSYS ecosystem, is built around RadixAttention, a technique that organizes the KV cache as a radix tree so shared prefixes across requests — system prompts, few-shot examples, conversation history — are computed once and reused. Around that runtime it offers a frontend language for structured generation, with constrained decoding and interleaved text and tool calls, plus an OpenAI-compatible server for drop-in deployment. Current releases target the workloads that dominate production AI: multi-turn agent traffic, large-scale serving and reinforcement-learning rollouts, and the repository bundles SGLang Diffusion, an image and video generation engine. Hardware support is unusually broad — NVIDIA GPUs from Blackwell down to consumer RTX cards, AMD Instinct, TPUs, NPUs, CPUs and Apple Silicon. The project is one of the most active in open-source inference, with updates continuing through October 2026.

Key Features

• RadixAttention – radix-tree KV cache management reuses shared prefixes across requests automatically.
• Structured generation – a frontend language with constrained decoding for reliable JSON and tool calls.
• Agent-ready serving – tuned for multi-turn agentic workloads and RL rollout generation.
• Multimodal and diffusion – serves vision-language models and includes an image/video generation engine.
• Broad hardware support – NVIDIA, AMD, TPU, NPU, CPU and Apple Silicon platforms.
• OpenAI-compatible server – deploy behind a familiar API from Docker or pip.
• Wide model coverage – Llama, Qwen, DeepSeek, GLM, Gemma and many more architectures.

💳 Pricing

SGLang is completely free and open source under the Apache License 2.0. You self-host it on your own accelerators, so the only cost is infrastructure.

Who Is It For

Infrastructure and ML platform teams serving open models at scale — especially agent platforms, RL training pipelines and anyone whose traffic shares long prefixes.