openjev-sglang — security grade SAFE, quality 66/100

Security audit verdict: SAFE · quality 66/100

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 →

About openjev-sglang

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

jevllmstructured-generationsystemone

Quick Facts

Stars209
Forks22
LanguagePython
CategoryLLM Plugin
Quality Score66.269155881763/100
Open Issues2
Last Updated2026-09-18
Created2026-09-17
Platformspython
Est. Tokens~16k

Compatible Skills

These tools work well together with openjev-sglang for enhanced workflows:

  • reflex — semantic(0.27)+complementary+shared_fw(huggingface)+same_lang+similar_pop+shared_platform (67%)
  • jevmeter — semantic(0.32)+complementary+rare_topics+same_lang+similar_pop+shared_platform (66%)
  • NanoJev — semantic(0.21)+complementary+shared_fw(huggingface)+same_lang+similar_pop+shared_platform (65%)

openjev-sglang alternative? Top 6 similar tools

Looking for a openjev-sglang alternative? If you're comparing openjev-sglang with other llm plugin tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • distill by samuelfaj · ⭐ 677

    Distill is a lightweight coding agent harness and TUI built to get more done with FAR FEWER tokens 🔥

  • awesome-jev by yibie · ⭐ 496

    A curated list of public projects, integrations, and discussions built on Jev — TypeSafe AI's System One model

  • awesome-typesafe by AbdelStark · ⭐ 372

    A curated list of official resources and community projects for TypeSafe, System One models, and Jev.

  • jev-codex-router by 0xNatoshi · ⭐ 80

    Per-turn model & reasoning routing for Codex, driven by Jev (TypeSafe System One): picks the model, thinking d

  • agentic-radar by splx-ai · ⭐ 1.0k

    A security scanner for your LLM agentic workflows

  • awesome-devops-mcp-servers by rohitg00 · ⭐ 1.0k

    A curated list of awesome MCP servers focused on DevOps tools and capabilities.

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Frequently Asked Questions

What is openjev-sglang?

openjev-sglang is Jev-compatible API endpoint based on open models (prefill-only). It is categorized as a LLM Plugin with 209 GitHub stars.

What programming language is openjev-sglang written in?

openjev-sglang is primarily written in Python. It covers topics such as jev, llm, structured-generation.

How do I install or use openjev-sglang?

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.

What are the best alternatives to openjev-sglang?

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.

How this security grade is produced

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:

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