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local-llm
A comprehensive guide for building and configuring a high-end local machine to run state-of-the-art LLMs, with detailed hardware choices, BIOS tuning, and Docker-based model serving.
Why it is worth attention
It bridges two formerly separate fields—knowledge graphs and agent orchestration—into a cohesive practice, grounded in a long-running university course and validated by recent research from Google DeepMind and MIT.
Who it is for
- AI engineers building agentic systems
- Knowledge graph practitioners seeking modern patterns
- Students and researchers in AI and data engineering
- Developers using skill-compatible agent frameworks like Claude Code
Use cases
- Building a knowledge graph from a set of documents using a structured 9-stage pipeline
- Teaching graph engineering interactively with your own project as the running example
- Designing task graphs with diamond splits, separate verifiers, and human gates
- Evaluating when agent teams benefit from splitting work vs. sequential execution
Strengths
- Includes a ready-to-use skill file that teaches the full pipeline to compatible agents
- Based on a proven graduate course (4.4K stars) and backed by empirical research on agent scaling
- Clear 9-stage pipeline with verification at every stage, avoiding the 'pile of triples' pitfall
- Provides paste-ready prompt blocks for both teaching and single-purpose tools
Considerations
- Requires a skill-compatible agent harness (currently demonstrated for Claude Code only)
- Original course material is in Chinese; the repo provides English distillation but does not redistribute original PDFs
- Task-graph rules are drawn from emerging research and may evolve as the field matures
README quick start
Graph Engineering
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.
What's inside
| 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. |
Install (two commands)
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 exa
Description
Graph engineering for AI agents: the 9-stage knowledge-graph pipeline (translated from SEU's graduate course) + task-graph orchestration patterns, as a Claude skill with teaching mode and paste-ready workflows
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