AgentSys
A modular runtime and orchestration system for AI agents.
19 plugins · 47 agents · 40 skills (across all repos) · 30k lines of lib code · 3,583 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:
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.
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 385 rules for Claude Code, Codex, OpenCode, Cursor, Kiro, Copilot, Gemini CLI, Cline, Windsurf, Roo Code, Amp, and more.
What This Is
An agent orchestration system — 19 plugins, 47 agents, and 40 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.
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.
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
Same task, same repo, same prompt ("I want to improve docs"):