OpenAgentd: Open-source desktop app for a local coding agent

by lthoangg · MCP Server · ★ 217

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

How to install OpenAgentd

uv tool install openagentd        # recommended
git clone https://github.com/lthoangg/openagentd.git

Taken from the project's README on GitHub.

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.

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.

agentsaiai-coding-assistantcoding-agentdesktop-appllmlocal-firstmcpmulti-agentollama

Quick Facts

Stars217
Forks38
LanguageTypeScript
CategoryMCP Server
LicenseApache-2.0
Quality Score70/100
Open Issues1
Last Updated2026-11-06
Created2026-04-30
Platformsmcp, node
Est. Tokens~17k

About OpenAgentd

OpenAgentd Your on-machine multi-agent system. A long-running local service with a web cockpit, persistent memory, and a team of agents that coordinate to get real work done. Everything stays on your hardware. Documentation What you get A cockpit, not a chat box.

Compatible Skills

These tools work well together with OpenAgentd for enhanced workflows:

  • agent-builder — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (56%)
  • Octopoda-OS — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (56%)
  • auto-browser — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (56%)

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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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