A locally compiled runtime for governed domain-agnostic agent graphs.

🐙 Graph engineering for long-horizon agents — a curated prompt library wiring specialized clean-context roles (executor · supervisor · scout) into a graph, compiled to each host (Claude Code · grok · Cursor · Codex). Arms: loop-graph · quest.

Desktop app for harness engineering, loop engineering, graph engineering—and whatever comes next in local AI-agent workflows.

🕸️ Field guide, open dataset & interactive atlas for graph-structured multi-agent systems and programmable AI-agent organizations.

Design grounded graphs of governed improvement loops.

Graph Engineering for Agent Skills: a specification and toolchain for dynamically discovering context and building observable, testable, and recoverable agent workflows.

Python toolkit for multi-step AI/agent systems as explicit graphs — define nodes/edges, structural validate (V1–V9), Mermaid visualize, pattern init, and skeleton walk. Vendor-agnostic. Runtime agent execute later.

An incident response copilot built with LangGraph, parallel investigation, human-in-the-loop approval, and long-term memory that improves with each resolved incident.

Central dogma of biology as map for Prompt→Loop→Graph→Evolution trajectory of AI agents. Skill framework, research scaffold, practical patterns.

Progress-aware execution for durable, grounded, token-efficient coding agents.

The coding-agent workbench where a deterministic scheduler — not the model — owns control flow. Orchestrates Claude Code & Codex through one auditable, resumable control plane.

Build graph-structured multi-agent systems with this collection of research papers, datasets, and design patterns for programmable AI organizations.