Verified claim · AI-ML · 100% confidence
SGLang introduced in: Zheng et al. 2024 — efficient LLM serving with structured outputs.
Last verified 2026-05-16 · Methodology veritas-v0.1 · 4244c11611a72550
Structured fields
- Subject
- SGLang
- Predicate
introduced_in- Object
- Zheng et al. 2024 — efficient LLM serving with structured outputs
- Confidence
- 100%
- Tags
- sglang · uc-berkeley · inference · structured-outputs · open-source · 2024 · introduced_in
Sources (2)
[1] preprint · arXiv (Zheng, Yin, Xie, Huang, Yu, Liu, Lin, Cuenca, Zhao, Stoica / UC Berkeley) · 2023-12-12
SGLang: Efficient Execution of Structured Language Model Programs“We introduce SGLang, a framework for efficient programming and execution of structured language model programs. SGLang consists of a frontend language and a runtime. The frontend simplifies programming with primitives for generation and parallelism control. The runtime accelerates the execution with optimizations like RadixAttention for KV cache reuse.”
[2] github release · SGLang Project / UC Berkeley · 2024-01-01
SGLang — official GitHub repository
Cite this claim
Ready-to-paste citation (Markdown / plain text):
SGLang introduced in: Zheng et al. 2024 — efficient LLM serving with structured outputs. — SourceScore Claim 4244c11611a72550 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/4244c11611a72550.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the signed claim + primary source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/4244c11611a72550/" width="100%" height="360" frameborder="0" loading="lazy" title="SGLang introduced in: Zheng et al. 2024 — efficient LLM serving with structured outputs."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
vLLM introduced in: Kwon et al. 2023 — high-throughput LLM serving via PagedAttention.
468a9e2c047d8f2f · 100% confidence · shares 4 tags (uc-berkeley, inference, open-source…)
OLMo released on: 2024-02-01 by Allen Institute for AI.
db733143bb054d41 · 100% confidence · shares 2 tags (open-source, 2024)
llama.cpp publicly released on: 2023-03-10 by Georgi Gerganov.
2c6ddc094019890c · 100% confidence · shares 2 tags (inference, open-source)
Chatbot Arena introduced in: Zheng et al. 2023 — LMSYS open platform for evaluating LLMs by human preference.
789ddc9bc9c3d688 · 100% confidence · shares 2 tags (uc-berkeley, introduced_in)
GPTQ introduced in: Frantar et al. 2022 — accurate post-training quantization for GPT models.
a9ab1ec12062f7ae · 100% confidence · shares 2 tags (inference, introduced_in)
Use this claim in your code
Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
cURL
curl https://sourcescore.org/api/v1/claims/4244c11611a72550.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/4244c11611a72550.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "SGLang introduced in: Zheng et al. 2024 — efficient LLM serving with structured outputs."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/4244c11611a72550.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "SGLang introduced in: Zheng et al. 2024 — efficient LLM serving with structured outputs."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_sglang_fact() -> dict:
"""Fetch the verified SourceScore claim for SGLang."""
r = httpx.get("https://sourcescore.org/api/v1/claims/4244c11611a72550.json")
return r.json()