compose-for-agents: Build and run AI agents using Docker Compose

by docker · Agent Tool · ★ 1.1k

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Security audit verdict: SAFE · quality 70/100

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.

agentic-workflowsai-agentsdockerdocker-composeexampleslarge-language-modelsllama-cppopenai-gymself-hosted

Quick Facts

Stars1,060
Forks477
LanguageTypeScript
CategoryAgent Tool
LicenseApache-2.0
Quality Score70/100
Open Issues28
Last Updated2026-10-23
Created2025-05-09
Platformsdocker, node
Est. Tokens~15k

About compose-for-agents

Compose for Agents Demos Prerequisites [Docker Desktop] 4.43.0+ or [Docker Engine] installed. A laptop or workstation with a GPU (e.g., a MacBook) for running open models locally. If you don't have a GPU, you can alternatively use [Docker Offload].

Compatible Skills

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

  • harbor — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • maid — semantic(0.19)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)
  • minima — semantic(0.28)+complementary+rare_topics+similar_pop+shared_platform (49%)
  • openclaw-mcp — semantic(0.27)+complementary+rare_topics+same_lang+shared_platform (49%)
  • phpMyFAQ — semantic(0.24)+complementary+rare_topics+similar_pop+shared_platform (48%)

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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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