SGLang

What is SGLang?

Getting a model to emit structured output — strict JSON, a function call — has always been fiddly. No matter how many times your prompt says "output valid JSON", the model sometimes drops a comma or adds a stray sentence. SGLang is an open-source framework built for exactly this. It gives you a small "structured generation language" so you can declare in code that "this field is an array" or "this value must be one of these options", while a fast inference backend keeps the whole thing quick.

Why is it fast?

RadixAttention: reuse the cache
Normally, if two requests differ even a little, the KV cache has to be recomputed. SGLang keeps shared prefixes in a radix tree and reuses them, so similar requests skip a lot of duplicate work.
Frontend language and backend engine designed together
It isn't just a thin wrapper. It designs "how you express" and "how it runs" as one system, so constraint checking and generation scheduling cooperate.

What problems does it actually solve?

Output that comes out in the right shape
Ask for JSON and you get valid JSON; ask for an enum and it won't invent a new value. That matters a lot in agent work, where downstream code has to parse your output to do anything.
Handles high concurrency
Shared-prefix caching cuts cost and latency for batched requests and multi-turn chats.

How is it different from a prompt that just says "output JSON"?

Asking nicely is a gamble — the model may occasionally disobey. SGLang works at the sampling level, restricting the next token to a legal set. That's a guarantee at the root, not a polite request.

Bottom line: SGLang pairs a structured-generation language with prefix caching so a model's complex output lands fast, stable, and well-formed.