---

name: sglang description: "Use SGLang for high-throughput LLM and VLM serving, OpenAI-compatible APIs, frontend language programs, runtime/server arguments, benchmarking, profiling, model/kernel extension, and SGLang repository development." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

SGLang

Use this skill when the task names SGLang, sglang, sglang serve, SRT, OpenAI-compatible serving, ServerArgs, radix cache, tensor/expert parallel serving, speculative decoding, SGLang frontend programs, benchmarking, profiling, custom model support, CUDA/JIT kernels, or development inside the SGLang repository.

Route By Task

• Serving runtime: use sub-skills/serving-runtime/SKILL.md for server launch, CLI flags, ServerArgs, tokenizer/engine settings, OpenAI-compatible endpoints, cache/scheduling, parallelism, disaggregation, and runtime troubleshooting.
• Frontend programming: use sub-skills/frontend-programming/SKILL.md for @sgl.function, gen, select, chat roles, backend/runtime endpoints, structured generation, multimodal inputs, and provider-backed frontend execution.
• Benchmarking and profiling: use sub-skills/benchmarking-profiling/SKILL.md for bench_serving, bench_one_batch, auto_benchmark, profiler scripts, latency/throughput metrics, dataset choices, and reproducible performance triage.
• Models and kernels: use sub-skills/model-kernel-extension/SKILL.md for adding model configs, extending serving backends, custom kernels, JIT kernels, CUDA attention work, quantization-related kernels, and cookbook/model support workflows.
• Repository development: use sub-skills/repo-development/SKILL.md for source checkout work, test selection, CI conventions, docs/cookbook changes, dependency caveats, and existing .claude/skills guidance.

Environment Notes

SGLang is a heavyweight serving stack. Full runtime installs can require pinned torch, CUDA/GPU packages, flashinfer, kernels, and other large wheels. Start with source/config inspection and help-only checks when a full serving environment is not already available. Do not promise that CPU-only or partial installs can run the production server.

References

• references/workflow-map.md maps common serving, frontend, benchmark, model, and repository tasks to sub-skills.
• references/troubleshooting.md covers dependency, CUDA, server, OpenAI API, frontend, benchmark, and repo-development failure modes.
• references/repo-provenance.md records the source revision and extraction evidence.
• scripts/sglang_source_smoke.py performs source-tree checks without importing the full heavyweight runtime.