graph-engineering

An open-source graph engineering runtime that keeps orchestration in TypeScript and delegates semantic work to replaceable Agent runtimes.

A Survey on Ontology Engineering, Graph Engineering, Loop Engineering, Harness Engineering, Context Engineering and Prompt Engineering

A context graph which is based on confidence-aware belief that drives more determinstic reasoning in long-horizon agents.

[Up-to-date] A curated list of resources on graph-empowered agents and agent-facilitated graph learning (Graphs Meet Agents & Agentic Graph Engineering).

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

Graph Engineering, simplified — with GraphCode. #graphcode

agent wiki +engineering skills

🦦 ADHDev — Agent Dashboard Hub. Monitor & control AI coding agents from a single dashboard. Self-hosted, open-source.

Deterministic memory layer for your agents

Build stateful agent workflows with typed outputs, reusable tools, session forks, and ordinary TypeScript.

A standalone durable graph router with explicit controls and no runtime dependencies.

Personal-memory-driven AI work twin that uses Codex to work inside real projects.

An end to end implementation of Graph Engineering as proposed by Andrew NG and Peter Steinberger

Long-horizon agent skill for Claude Code / Cursor / Codex / Grok Build — multi-task ledger loop, host-portable, clean-context supervisor, verified gates. Markdown library (loop-graph), not a framework.

🕸️ Engineer the organization, not just the agent. 620 curated resources · 9 design layers · 11 sections · 298 papers & preprints — a field guide, CC0 open dataset, and interactive atlas for graph-structured multi-agent systems: roles, topologies, handoffs, work graphs, state, gates, reliability, observability.

A graph-engineering agent harness for long-horizon, research-level autoformalization.

This repository serves as the comprehensive workspace for Quarter 4-5 academic endeavors, encompassing assignments, technical documentation, experimental implementations, and applied projects.

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

An executable, no-code SDLC graph that carries a feature from spec to PR — with explicit nodes, guards, bounded retry loops, and durable state. No framework. No SDK. No runtime to install. Your coding agent is the runtime.

Kiro as a Software Factory: graph engineering with agent loops that run turn after turn as kiro-cli sessions, wired together through folders on disk. One loop writes a file, the next picks it up atomically; queues, topics and clusters shape the graph, each loop runs under an enforced tool grant, and it all lives in a directory you point it at.