agentsys

AgentSys

A modular runtime and orchestration system for AI agents.

24 plugins · 49 agents · 44 skills (across all repos) · 30k lines of lib code · 3,518 tests · 5 platforms
Plugins distributed as standalone repos under agent-sh org - agentsys is the marketplace & installer

Commands · Installation · Website · Discussions

Built for Claude Code · Codex CLI · OpenCode · Cursor · Kiro

New skills, agents, and integrations ship constantly. Follow for real-time updates:

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AI models can write code. That's not the hard part anymore. The hard part is everything around it - task selection, branch management, code review, artifact cleanup, CI, PR comments, deployment. AgentSys is the runtime that orchestrates agents to handle all of it - structured pipelines, gated phases, specialized agents, and persistent state that survives session boundaries.

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Building custom skills, agents, hooks, or MCP tools? agnix is the CLI + LSP linter that catches config errors before they fail silently - real-time IDE validation, auto suggestions, auto-fix, and 423 rules for Claude Code, Codex, OpenCode, Cursor, Kiro, Copilot, Gemini CLI, Cline, Windsurf, Roo Code, Amp, and more.

What's New in 6.0.1

• Restores Cursor and Kiro installation after a shared-core regression.
• Removes the unused vulnerable js-yaml production dependency.

What This Is

An agent orchestration system - 24 plugins, 49 agents (39 file-based + 10 role-based specialists in audit-project), and 44 skills that compose into structured pipelines for software development. Each plugin lives in its own standalone repo under the agent-sh org. agentsys is the marketplace and installer that ties them together.

Each agent has a single responsibility, a specific model assignment, and defined inputs/outputs. Pipelines enforce phase gates so agents can't skip steps. State persists across sessions so work survives interruptions.

The system runs on Claude Code, OpenCode, Codex CLI, Cursor, and Kiro. Install via the marketplace or the npm installer, and the plugins are fetched automatically from their repos.

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The Approach

Code does code work. AI does AI work.

• Detection: regex, AST analysis, static analysis - fast, deterministic, no tokens wasted
• Judgment: LLM calls for synthesis, planning, review - where reasoning matters
• Result: 77% fewer tokens for /drift-detect vs multi-agent approaches, certainty-graded findings throughout

Certainty levels exist because not all findings are equal:

Level	Meaning	Action
HIGH	Definitely a problem	Safe to auto-fix
MEDIUM	Probably a problem	Needs context
LOW	Might be a problem	Needs human judgment

This came from testing on 1,000+ repositories.

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Benchmarks

Structured prompts and enriched context do more for output quality than model tier. Benchmarked March 2026 on real tasks (/can-i-help and /onboard against glide-mq), measured with claude -p --output-format json. Models: Claude Opus 4 and Claude Sonnet 4.

Sonnet + AgentSys vs raw Opus