No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by ekzhang · LLM Plugin · ★ 209
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is openjev-sglang safe to install? View the security audit →
openjev-sglang A server implementing the TypeSafe/Jev HTTP API with Qwen3.6-35B-A3B on SGLang. Each container one B200 with SGLang 0.5.19's Rust frontend, radix caching, and breakable prefill CUDA graphs. A separate Python API process uses FastAPI, uvloop, the Rust-backed HF tokenizer, and pooled asynchronous HTTP connections to SGLang on localhost. CUDA dependencies stay in SGLang's container; on your laptop installs only the API, deployment tools, and tests. Run on Modal The deployment prints a URL. It uses a Modal Server, , , and . Autoscaling has no explicit container cap and scales to zero after five idle minutes. Set in to keep a B200 warm. If SGLang exits unexpectedly, the API exits too. The Modal launcher watches the API and exits the container so Modal can replace it, rather than leaving a live HTTP process with a dead inference backend. Normal shutdown disarms both watchers. Cache warmups also requ
| Stars | 209 |
| Forks | 22 |
| Language | Python |
| Category | LLM Plugin |
| Quality Score | 66.269155881763/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-18 |
| Created | 2026-09-17 |
| Platforms | python |
| Est. Tokens | ~16k |
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openjev-sglang is Jev-compatible API endpoint based on open models (prefill-only). It is categorized as a LLM Plugin with 209 GitHub stars.
openjev-sglang is primarily written in Python. It covers topics such as jev, llm, structured-generation.
You can find installation instructions and usage details in the openjev-sglang GitHub repository at github.com/ekzhang/openjev-sglang. The project has 209 stars and 22 forks, indicating an active community.
The top alternatives to openjev-sglang on Agent Skills Hub include distill, awesome-jev, awesome-typesafe. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: