cy0307/awesome-graph-engineering

Awesome Graph Engineering Resource Atlas

A versioned collection of research, standards, frameworks, protocols, reliability systems, evaluations, and critiques for graph-structured multi-agent systems and programmable AI-agent organizations.

This dataset mirrors Awesome Graph Engineering. The GitHub JSONL file is canonical; the Hub exposes the same records through Dataset Viewer, direct downloads, datasets, and pandas.

Working definition

Graph engineering is the practice of specifying, executing, observing, and evolving a graph-structured agent system—its roles and runtime instances, the contracts that connect them, the state and artifacts they share, and the evidence by which their collective behavior is judged—so that the system can be controlled, tested, and improved as an engineered whole.

The graph must be load-bearing rather than decorative. Its declared topology, realized run graph, or graph-generating policy must materially constrain execution and remain inspectable enough to version, trace, evaluate, or deliberately change.

Graph engineering is used here as an emerging, non-standard term. The scope synthesizes multi-agent research, historical agent-system foundations, standards, runtime documentation, benchmarks, negative results, and practitioner analysis. The evidence map separates source-backed claims from analytical inferences. Carlos E. Perez’s “graph of loops” essay is one contemporary source, not an authority for the definition.

Dataset structure

The default configuration contains one train split. “Train” is the Hub's conventional single-split label; these rows are a resource atlas, not labeled examples or a model-training benchmark.

Each row has 13 fields:

resources.jsonl drives the Dataset Viewer. resources.csv is an equivalent convenience export in the same row and field order. resource.schema.json is the machine-readable JSON Schema for one record.

Interoperable access points:

• —Dataset Viewer
• —Croissant metadata
• —Auto-converted Parquet

Load the data

from datasets import load_dataset

dataset = load_dataset("cy0307/awesome-graph-engineering")
resources = dataset["train"]
print(resources.num_rows, resources.column_names)

import pandas as pd

url = (
    "https://huggingface.co/datasets/cy0307/awesome-graph-engineering/"
    "resolve/main/resources.csv"
)
resources = pd.read_csv(url)
print(resources.groupby("layer").size().sort_values(ascending=False))

Intended uses

• —discover primary sources and implementation references by engineering layer;
• —seed literature reviews and architecture comparisons, followed by reading the linked originals;
• —analyze how the collection is distributed across source types, years, sections, and layers;
• —build educational tools, resource browsers, or retrieval indexes; and
• —propose corrections and additions through the canonical GitHub repository.

Limitations and responsible use

• —The collection is selective and versioned; it is not an exhaustive scrape or systematic review.
• —evidence records publication form. It does not score correctness, replication, maintenance, safety, or endorsement.
• —Official documentation is authoritative about intended product behavior, not independent proof of reliability.
• —Practitioner analysis may define or challenge a boundary, but recency does not give it priority over primary evidence.
• —The field and its vocabulary are changing quickly; verify current documentation, publication status, licenses, and security posture before adoption.
• —Linked resources are catalogued by URL only; verify each resource’s rights and license before reuse.

Read the full curation methodology, scope boundaries, and dataset contract before drawing aggregate conclusions.

License

The dataset metadata, schema, original summaries, and repository-created assets are dedicated under CC0 1.0 Universal. Linked resources are not included in that dedication and remain subject to their own rights and licenses. CC0 does not waive trademark or patent rights and provides the work without warranties. Citation is appreciated for scholarly traceability but is not required by CC0.

Versioning and synchronization

GitHub’s data/resources.jsonl is canonical. The CSV, README tables, interactive atlas, and Hub mirror are generated from it. Stable IDs are never recycled. When HF_TOKEN is configured, accepted changes to main trigger validation and mirror publication.

Citation

Curated by He Chaoyue.

@misc{he2026awesomegraphengineering,
  author       = {He, Chaoyue},
  title        = {Awesome Graph Engineering: A Field Guide, Dataset, and Interactive Atlas for Programmable AI-Agent Organizations},
  year         = {2026},
  version      = {1.3.0},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/ChaoYue0307/awesome-graph-engineering/releases/tag/v1.3.0}},
  url          = {https://github.com/ChaoYue0307/awesome-graph-engineering/releases/tag/v1.3.0}
}

Machine-readable metadata is available in `CITATION.cff`. Cite the original linked works for claims derived from them.