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 EvolutionAPI · MCP Server · ★ 556
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is evo-ai safe to install? View the security audit →
Evo AI - AI Agents Platform Evo AI - AI Agents Platform Evo AI is an open-source platform for creating and managing AI agents, enabling integration with different AI models and services. 🚀 Overview The Evo AI platform allows: Creation and management of AI agents Integration with different language models Client management and MCP server configuration Custom tools management Google Agent Development Kit (ADK): Base framework for agent development CrewAI Support: Alternative framework for agent development (in development) JWT authentication with email verification [Agent 2 Agent (A2
| Stars | 556 |
| Forks | 174 |
| Language | TypeScript |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 70.8821035016074/100 |
| Open Issues | 20 |
| Last Updated | 2025-06-02 |
| Created | 2025-05-13 |
| Platforms | mcp, node |
| Est. Tokens | ~7539k |
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evo-ai is Evo AI is an open-source platform for creating and managing AI agents, enabling integration with different AI models and services.. It is categorized as a MCP Server with 556 GitHub stars.
evo-ai is primarily written in TypeScript. It covers topics such as a2a-protocol, adk, agent.
You can find installation instructions and usage details in the evo-ai GitHub repository at github.com/EvolutionAPI/evo-ai. The project has 556 stars and 174 forks, indicating an active community.
evo-ai is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to evo-ai on Agent Skills Hub include oreilly-ai-agents, flock, argo. 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: