agent-control: High-performance AI Agent Control Plane, Task Orchestration & Model Context Protocol (MCP) Platform

by loulanyue · MCP Server · ★ 177

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install agent-control

git clone https://github.com/loulanyue/agent-control.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 69/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agent-control-planeai-agentautonomous-agentscrawler-pipelinedag-workflowfastapimcp-servermodel-context-protocoltask-orchestrationworkflow-engine

Quick Facts

Stars177
Forks6
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score69/100
Last Updated2026-10-14
Created2026-09-29
Platformsdocker, mcp, python
Est. Tokens~15k

About agent-control

🚀 Agent Control Modern, High-Performance Control Plane & Task Orchestration Platform for Autonomous AI Agents English | 中文说明 🌟 Highlights Agent Control is a production-grade, asynchronous AI Agent Control Plane and orchestration engine designed to manage autonomous agents, distributed task dispatching, heartbeat leasing, DAG workflow execution, and data collection pipelines at scale.

Compatible Skills

These tools work well together with agent-control for enhanced workflows:

  • agentic-career-search — semantic(0.19)+complementary+shared_fw(fastapi)+same_lang+similar_pop+shared_platform (65%)
  • claude-ctrl — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (57%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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