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 SonicBotMan · MCP Server · ★ 67
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is SoloFlow safe to install? View the security audit →
SoloFlow ⚡ The Brain Behind AI Workflow Orchestration Turn chaotic multi-step AI tasks into structured, observable, retryable workflows — with cognitive memory, discipline-aware routing, and automatic skill evolution. Why SoloFlow? AI Agents fail in predictable ways: Four Pillars DAG + FSM Hybrid Architecture Kahn algorithm for topological sorting Parallel execution where possible, sequential where required Automatic retry with exponential backoff Cognit
| Stars | 67 |
| Forks | 6 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 73.5373268871103/100 |
| Open Issues | 1 |
| Last Updated | 2026-05-31 |
| Created | 2026-03-05 |
| Platforms | mcp, python |
| Est. Tokens | ~179k |
These tools work well together with SoloFlow for enhanced workflows:
Looking for a SoloFlow alternative? If you're comparing SoloFlow with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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SoloFlow is Complete ETCLOVG framework for AI Agent workflows - DAG+FSM orchestration, Ebbinghaus memory, discipline routing, skill evolution, trace system, governance. 80+ tests, zero deps, 7/7 layers.. It is categorized as a MCP Server with 67 GitHub stars.
SoloFlow is primarily written in Python. It covers topics such as agent-harness, ai-agent, cognitive-memory.
You can find installation instructions and usage details in the SoloFlow GitHub repository at github.com/SonicBotMan/SoloFlow. The project has 67 stars and 6 forks, indicating an active community.
The top alternatives to SoloFlow on Agent Skills Hub include yantrikdb, omnicoreagent, maestro. 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: