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

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

agent wiki +engineering skills

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

Turns repeatable, domain-agnostic workflows into multi-step graph-driven loops.

Long-horizon agent skill for Claude Code / Cursor / Codex / Grok — multi-task ledger loop (related or not), host-portable (re-send the prompt to continue), clean-context supervisor, verified gates. Markdown library (loop-graph · quest), not a framework.

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

🕸️ Engineer the organization, not just the agent. 455 curated resources · 9 design layers · 11 sections · 207 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.

Design grounded graphs of governed improvement loops.

Copy-paste prompts that turn your AI agents from a waiting line into a graph: a 5-min demo, false-edge audit, diamond research, adversarial review, consultant roundtable, and an issue tree that dispatches itself. EN + 繁中.

Installable graph engineering for Claude Code, Codex, OpenCode, and Cursor — dependency-graph execution with local caching, quality gates, selective retries, and live reports

Design the structures your agents work through — knowledge graphs for memory, task graphs for orchestration. Playbook + agent skill + runnable stdlib-only pipeline.

A production-grade Python & Streamlit reference implementation of the 5-Layer Graph Engineering Taxonomy, implementing the complete technical outline

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

Bounded static DAG contracts, validators, experiments, and adjacent agent architecture boundaries.

从 Prompt 到 Graph Engineering：五层 Agent 工程手册与可运行示例

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.

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

Interactive GitHub issue dependency DAG and issue explorer

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