managed-agents: Open-source CMA-compatible agent runtime

by sandbaseai · MCP Server · ★ 440

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

How to install managed-agents

npx managed-agents init
npx managed-agents start

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 71/100

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.

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.

Quick Facts

Stars440
Forks40
LanguageTypeScript
CategoryMCP Server
Quality Score71/100
Last Updated2026-08-01
Created2026-07-11
Platformsclaude-code, mcp, node
Est. Tokens~17k

About managed-agents

managed-agents SandBase managed-agents is the safe, local-first runtime layer for enterprise AI agents. helps teams move AI agents from demos to production with runtime infrastructure for sessions, tools, approvals, sandboxed execution, memory, credential vaults, audit trails, replayable events, and operational visibility.

Compatible Skills

These tools work well together with managed-agents for enhanced workflows:

  • HydraTeams — semantic(0.18)+complementary+shared_fw(ollama)+same_lang+similar_pop+shared_platform (64%)
  • agent88 — semantic(0.17)+complementary+shared_fw(ollama)+same_lang+similar_pop+shared_platform (64%)
  • agent-canvas — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (58%)

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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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