SGLang

In this catalog it is the Serving option when you need to run model inference at usable latency and cost. Keep SGLang on the observe/propose side of the loop: useful signals and drafts, with human or policy authority still required before anything irreversible runs.

SGLang serves LLMs with structured generation and radix-attention caching for high-throughput apps.

1. Launch the SGLang runtime with a model.
2. Clients submit generation / structured programs.
3. Radix cache reuses shared prefixes across requests.
4. Return tokens or structured outputs.

Related: vllm · topics/12-central-nervous-system · topics/04-data

• Use when: SGLang is a fast serving framework with efficient structured generation and radix attention caching for high-throughput LLM apps matches the bottleneck and you specifically need to run model inference at usable latency and cost.
• Skip when: another pack already covers the need with less ops surface, or SGLang would become a second place people retype truth.
• Prefer instead: compare vLLM before locking the cell.

• Ops: confirm license, hosting, and release cadence for SGLang (sgl-project/sglang) before relying on it in a live corridor.
• Integration: bad credentials, scopes, or schema drift produce empty retrievals or runaway tool/model spend without an obvious UI alarm.
• Scope: Serving components support agents; they are not the company ledger or the approval authority.