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 omnirexflora-labs · MCP Server · ★ 246
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is omnicoreagent safe to install? View the security audit →
OmniCoreAgent The Open Production Agent Harness for Python Parallel tool batches, structured observations, signature loop detection, MCP tools, memory, workspace files, subagents, background tasks, and REST/SSE serving. What It Is - Quick Start - Choose Your Path - Use Cases - Why It Matters - Install - Cookbook - Features - Docs - Ask AI
| Stars | 246 |
| Forks | 58 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70.7850579835299/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-22 |
| Created | 2025-03-19 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~18k |
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omnicoreagent is Open Python agent harness for production AI apps: tools, MCP, memory, workspace, telemetry, subagents, background tasks, and OmniServe APIs.. It is categorized as a MCP Server with 246 GitHub stars.
omnicoreagent is primarily written in Python. It covers topics such as agent, agent-harness, ai-agents.
You can find installation instructions and usage details in the omnicoreagent GitHub repository at github.com/omnirexflora-labs/omnicoreagent. The project has 246 stars and 58 forks, indicating an active community.
omnicoreagent is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to omnicoreagent on Agent Skills Hub include OmoiOS, Autono, entroly. 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: