SGLang
An LLM inference and programming framework optimized for structured generation, agent workloads, and complex prompting patterns — competitive with vLLM on throughput and faster on JSON/grammar-constrained output.
SGLang (LMSYS, 2024) reframes LLM serving around the patterns agents actually need: structured generation, tool calls, parallel completions, prefix sharing across related prompts. Its compiler analyzes a program's prompt structure to pre-share KV cache across calls.
On structured output workloads (JSON, regex-constrained, grammar-constrained), SGLang typically beats vLLM by 2–10× because it precomputes the constraint masks alongside generation. For free-form text, the two are close.
In 2026, SGLang is the default for any team running open-source models behind agent workflows. vLLM still wins on community size and broadest model coverage; SGLang wins on agent-shaped throughput.
Frequently asked
Should I move from vLLM to SGLang?+
Move if structured outputs are the bottleneck — JSON-heavy agents, tool-use-heavy workloads. Stay on vLLM if you serve mostly free-form text or need maximum model coverage.