The discipline of designing the structures AI agents work through — not the prompts.
It has two halves:
- Knowledge graphs — what agents remember. Nodes are entities and facts, edges are relationships with time and provenance. Ontology → extraction → fusion → serving.
- Task graphs — how agents work. Nodes are jobs, edges are execution dependencies. Parallel fan-out, separate verifiers, the stop rule, the human gate.
Prompt engineers steered the model's words. Loop engineers steered its iterations. Graph engineers steer its topology.
This repo turns Southeast University's graduate Knowledge Graph course (npubird/KnowledgeGraphCourse, 4.4K★, taught in Chinese since 2019) — plus the modern agent-orchestration research behind task graphs — into things you can actually use today.
| Path | What it is |
|---|---|
graph-engineering/ |
The skill. Hand it to your agent (Claude Code / any skill-compatible harness) — it learns the full 9-stage knowledge-graph pipeline, the task-graph patterns, and a teaching mode that explains every stage with diagrams drawn from your domain. |
graph-engineering/references/ |
The distilled course: curriculum map (translated, with links to the original Chinese decks), modeling, extraction, fusion + GraphRAG, task graphs. |
WORKFLOWS.md |
Nine paste-ready prompt blocks — a /kg-tutor that teaches you the whole course interactively, plus eight single-purpose tools (/kg-scope → /kg-rag) that chain into a full build. |
dist/graph-engineering.skill |
The packaged skill file. |
git clone https://github.com/codejunkie99/graph-engineering.git
cp -r graph-engineering/graph-engineering ~/.claude/skills/Then ask your agent to build ("build a knowledge graph from my docs") or to teach ("teach me graph engineering") — teaching mode walks the pipeline stage by stage with worked examples and generated diagrams, using your own project as the running example.
flowchart LR
A[1 scope] --> B[2 representation] --> C[3 ontology] --> D[4 entities]
D --> E[5 relations] --> F[6 events] --> G[7 quality gate]
G --> H[8 fusion] --> I[9 serve to LLMs]
Model the domain before extracting. Fuse before storing. Verify at every stage. A knowledge graph is a product with a schema, not a pile of triples.
- Delete fake edges: an arrow is real only when work flows through it.
- The diamond: split → parallel workers → separate verifier contexts → one owned merge.
- The stop rule (Google DeepMind × MIT, 180 configurations): teams win ~80% on work that splits; every team configuration loses on sequential work. The shape of the work decides.
- The human gate: your approval sits exactly where a mistake is expensive to undo.
Details: references/task-graphs.md
The knowledge-graph half is an independent English distillation of 东南大学《知识图谱》研究生课程 (Southeast University's graduate Knowledge Graph course), Prof. Peng Wang — npubird/KnowledgeGraphCourse. All original lecture PDFs are in Chinese and remain in the original repo; none are redistributed here. Task-graph material draws on Google DeepMind × MIT's "Towards a Science of Scaling Agent Systems" and Anthropic's published multi-agent engineering work.
MIT licensed. Built by @Av1dlive.
