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 labsai · MCP Server · ★ 379
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is EDDI safe to install? View the security audit →
E.D.D.I — Multi-Agent Orchestration Middleware for Conversational AI E.D.D.I (Enhanced Dialog Driven Interface) is a production-grade, config-driven multi-agent orchestration middleware for conversational AI. It coordinates users, AI agents, and business systems through intelligent routing, persistent memory, and API orchestration — without writing code. Built with Java 25 and Quarkus. Ships as a Red Hat-certified Docker image. Native support for MCP (Model Context P
| Stars | 379 |
| Forks | 128 |
| Language | Java |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 63.7727599927809/100 |
| Open Issues | 61 |
| Last Updated | 2026-09-22 |
| Created | 2016-10-13 |
| Platforms | cli, docker, java, mcp |
| Est. Tokens | ~25k |
These tools work well together with EDDI for enhanced workflows:
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EDDI is Config-driven engine that turns JSON into production-grade AI agents. Multi-agent orchestration, 12+ LLM providers, MCP/A2A protocols, RAG, persistent memory, and enterprise compliance (EU AI Act, GDP. It is categorized as a MCP Server with 379 GitHub stars.
EDDI is primarily written in Java. It covers topics such as a2a, ai-agents, ai-orchestration.
You can find installation instructions and usage details in the EDDI GitHub repository at github.com/labsai/EDDI. The project has 379 stars and 128 forks, indicating an active community.
EDDI is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to EDDI on Agent Skills Hub include trpc-agent-go, solon-ai, boluobobo-ai-court-tutorial. 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: