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 Rath-Team · MCP Server · ★ 1.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenRath safe to install? View the security audit →
OpenRath English | 简体中文 OpenRath is a PyTorch-like multi-agent & multi-session framework. It turns agent runtime state into explicit, composable Python objects: Session carries conversation state and inter-agent collaboration lineage. Sandbox decides where tools actually run. Memory persists agent memory state across runs. Tool is the operator-like callable surface exposed to the model. Agent is a reusable, composable session transformation layer. Workflow composes multiple agents and workflows into larger systems. Selector routes between self-describing workflows at
| Stars | 1,142 |
| Forks | 59 |
| Language | Python |
| Category | MCP Server |
| License | BSD-3-Clause |
| Quality Score | 64.2851070741564/100 |
| Open Issues | 7 |
| Last Updated | 2026-09-28 |
| Created | 2026-05-04 |
| Platforms | mcp, python |
| Est. Tokens | ~17k |
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OpenRath is An open-source, PyTorch-like runtime for dynamic multi-agent and multi-session workflows.. It is categorized as a MCP Server with 1.1k GitHub stars.
OpenRath is primarily written in Python. It covers topics such as agent-framework, agentic-ai, ai-agents.
You can find installation instructions and usage details in the OpenRath GitHub repository at github.com/Rath-Team/OpenRath. The project has 1.1k stars and 59 forks, indicating an active community.
OpenRath is released under the BSD-3-Clause license, making it free to use and modify according to the license terms.
The top alternatives to OpenRath on Agent Skills Hub include ag2, ruby_llm, mcp-memory-service. 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: