otari — security grade SAFE, quality 70/100

Security audit verdict: SAFE · quality 70/100

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 mozilla-ai · LLM Plugin · ★ 487

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is otari safe to install? View the security audit →

About otari

An OpenAI-compatible LLM gateway you own and run yourself. Put one endpoint in front of 40+ providers, then manage API keys, enforce budgets, and track usage in one place. 📖 Docs · 🚀 otari.ai · 📝 Launch blog · 💬 Discord Otari is the proxy server at the heart of otari.ai. Your apps talk to Otari, which routes to your providers. Otari authenticates each request, enforces budgets before the call runs, resolves your provider credential, forwards the request, and logs the usage. Run it yours

aiai-gatewayanthropicapi-key-managementbudgetscost-trackinggatewaylitellm-alternativellmllm-gateway

Quick Facts

Stars487
Forks57
LanguagePython
CategoryLLM Plugin
LicenseApache-2.0
Quality Score69.9005151986075/100
Open Issues276
Last Updated2026-09-22
Created2026-04-02
Platformspython
Est. Tokens~17k

Compatible Skills

These tools work well together with otari for enhanced workflows:

  • NadirClaw — semantic(0.38)+complementary+shared_fw(openai)+rare_topics+same_lang+similar_pop+shared_platform (80%)
  • NadirClaw — semantic(0.38)+complementary+shared_fw(openai)+rare_topics+same_lang+similar_pop+shared_platform (80%)
  • ccproxy — semantic(0.33)+complementary+shared_fw(openai)+rare_topics+same_lang+similar_pop+shared_platform (79%)

otari alternative? Top 6 similar tools

Looking for a otari alternative? If you're comparing otari 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.

  • llmgateway by theopenco · ⭐ 1.7k

    Route, manage, and analyze your LLM requests across multiple providers with a unified API interface.

  • ccproxy by starbaser · ⭐ 350

    Build mods for Claude Code: Hook any request, modify any response, /model "with-your-custom-model", intelligen

  • busbar by GetBusbar · ⭐ 148

    The execution control plane for AI agents. Govern every model request, MCP tool call, A2A delegation, and down

  • NadirClaw by NadirRouter · ⭐ 653

    Open-source LLM router & AI cost optimizer. Routes simple prompts to cheap/local models, complex ones to premi

  • NadirClaw by doramirdor · ⭐ 336

    Open-source LLM router & AI cost optimizer. Routes simple prompts to cheap/local models, complex ones to premi

  • lm-proxy by Nayjest · ⭐ 150

    OpenAI-compatible HTTP LLM proxy / gateway for multi-provider inference (Google, Anthropic, OpenAI, PyTorch).

More LLM Plugin Tools

Explore other popular llm plugin tools:

View all LLM Plugin tools →

Popular Python Agent Tools

Frequently Asked Questions

What is otari?

otari is Open-source, OpenAI-compatible LLM gateway you run yourself. One endpoint for 40+ providers, with virtual keys, budgets, and usage tracking.. It is categorized as a LLM Plugin with 487 GitHub stars.

What programming language is otari written in?

otari is primarily written in Python. It covers topics such as ai, ai-gateway, anthropic.

How do I install or use otari?

You can find installation instructions and usage details in the otari GitHub repository at github.com/mozilla-ai/otari. The project has 487 stars and 57 forks, indicating an active community.

What license does otari use?

otari is released under the Apache-2.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to otari?

The top alternatives to otari on Agent Skills Hub include llmgateway, ccproxy, busbar. 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:

View on GitHub → Browse LLM Plugin tools