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 benavlabs · Agent Tool · ★ 70
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is clientai safe to install? View the security audit →
ClientAI A unified client for AI providers with built-in agent support. ClientAI is a Python package that provides a unified framework for building AI applications, from direct provider interactions to transparent LLM-powered agents, with seamless support for OpenAI, Replicate, Groq and Ollama. Documentation: benavlabs.github.io/clientai/ Features Unified Interface: Consistent methods across multiple AI providers (OpenAI, Replicate, Groq, Ollama). Streaming Support: Real-time response streaming and chat capabilities. Intelligent Agents: Framework for building transparent, multi-step LLM workflows with tool integration. Output Validation: Built-in validation sys
| Stars | 70 |
| Forks | 7 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 70.6409227723437/100 |
| Open Issues | 12 |
| Last Updated | 2025-07-06 |
| Created | 2024-10-02 |
| Platforms | cli, python |
| Est. Tokens | ~265k |
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clientai is A unified client for AI providers with built-in agent support.. It is categorized as a Agent Tool with 70 GitHub stars.
clientai is primarily written in Python. It covers topics such as agents, ai, ai-agents.
You can find installation instructions and usage details in the clientai GitHub repository at github.com/benavlabs/clientai. The project has 70 stars and 7 forks, indicating an active community.
clientai is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to clientai on Agent Skills Hub include mcp-server, c4-genai-suite, Awesome-AI-For-Security. 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: