pygfm is a unified Python toolkit for Graph Foundation Model (GFM) research. It integrates 17 state-of-the-art baseline methods under a single, pip-installable package with shared utilities, standardized interfaces, and fully reproducible experiment pipelines.
Developed by Beihang University · School of Computer Science and Engineering · ACT Lab · MAGIC GROUP.
PyGFM is organized into four stacked layers — Graph Data Abstraction → Alignment & Fusion Bridge → Representation Backbones → Task Heads & Orchestration — with a unified CLI, model recipes, and an auto-experiment tracker sitting on top.
- One package, 17 baselines — prompt-based GFMs, structure-aware models, LLM-integrated approaches, and retrieval-augmented methods all available via a single
pip install. - Reproducible pipelines — every baseline ships with YAML-driven experiment configs, training scripts, and evaluation helpers.
- Shared backbone library — common GNN encoders, loss functions, and data utilities are factored out and reused across all baselines, reducing code duplication.
- CLI-first design — launch pre-training, fine-tuning, and evaluation jobs directly from the command line without writing any boilerplate.
- LLM-ready — first-class support for LLM-integrated GFMs (GraphGPT, GraphText, LLaGA, OneForAll) with HuggingFace-compatible YAML configs.
Default (fresh env): torch + light together — PyTorch wheel index + PyPI + PyG find-links:
pip install "python-gfm[torch,light]" --index-url https://download.pytorch.org/whl/cu128 --extra-index-url https://pypi.org/simple -f https://data.pyg.org/whl/torch-2.8.0+cu128.htmlIf CUDA PyTorch / PyG is already in the env — install [light] from PyPI only:
pip install "python-gfm[light]"LLM-integrated GFMs — after [torch] and [light] are in place:
pip install "python-gfm[llm]"CPU:
--index-url https://download.pytorch.org/whl/cpuand-f https://data.pyg.org/whl/torch-2.8.0+cpu.html.
| Extra | Contents (short) |
|---|---|
torch |
PyTorch Geometric stack, graph libs, sklearn helpers |
light |
NumPy/Pandas stack, Transformers, Hydra, APIs, Gradio, W&B, SwanLab |
llm |
PEFT, bitsandbytes, datasets, fschat, Ray, Vertex, DeepSpeed |
pip install "python-gfm[dev]" adds pytest and ruff for testing and linting.
This repository is the PyPI package source. For installation, quick start, YAML configs, Hugging Face asset download, CLI commands, and per-baseline experiment steps, see the PyPI project page.
.
├── assets/ # Logo and framework figures
├── ckpts/ # Example / default checkpoint outputs
├── dist/ # Built wheel artifacts (release)
├── scripts/ # Per-baseline experiment scripts and configs
│ ├── bridge/
│ ├── gcot/
│ ├── graphgpt/
│ ├── graphkeeper/
│ ├── graphmore/
│ ├── graphprompt/
│ ├── graphtext/
│ ├── graver/
│ ├── hgprompt/
│ ├── llaga/
│ ├── mdgfm/
│ ├── mdgpt/
│ ├── multigprompt/
│ ├── oneforall/
│ ├── rag_gfm/
│ ├── sa2gfm/
│ └── samgpt/
├── src/pygfm/ # Installable package source
│ ├── baseline_models/ # GFM baseline implementations (17 methods)
│ ├── cli/ # Console entry points and YAML stage runners
│ ├── download/ # `python -m pygfm.download` module shim
│ ├── private/ # Core encoders and internal helpers
│ │ ├── core/
│ │ └── utlis/ # Domain alignment, RAG builders, data gen, etc.
│ ├── public/ # Shared utilities exposed across baselines
│ │ ├── backbone_models/ # Reusable GNN encoders
│ │ ├── cli/ # YAML / config helpers
│ │ └── utils/ # Data, loss, LLM, and misc helpers
│ ├── tools/ # In-package maintenance utilities
│ ├── tool_download.py # Hugging Face dataset downloader
│ ├── _scripts_bundle.zip # Packaged copy of `scripts/` (shipped in wheel)
│ └── __main__.py # `python -m pygfm` entry (run YAML / download)
├── tools/ # Repo-level build helpers (e.g. wheel bundling)
├── pyproject.toml # Package metadata and dependency pins
└── README.md
After pip install, the installed package exposes the same pygfm/ tree (with scripts/ bundled as _scripts_bundle.zip inside the wheel).
| Category | Methods |
|---|---|
| Prompt-based GFM | MDGPT, SAMGPT, MDGFM, GraphPrompt, HGPrompt, MultiGPrompt, GCoT |
| Structure-aware GFM | SA2GFM, Bridge, GraphKeeper, GraphMore, Graver |
| LLM-integrated GFM | GraphGPT, GraphText, LLaGA, OneForAll |
| Retrieval-augmented GFM | RAG-GFM |
Per-method setup, data layout, and evaluation notes live under scripts/<baseline>/. Index:
| Baseline | Docs |
|---|---|
| MDGPT | scripts/mdgpt/README.md |
| SA2GFM | scripts/sa2gfm/README.md |
| SAMGPT | scripts/samgpt/README.md |
| MDGFM | scripts/mdgfm/README.md |
| GraphPrompt | scripts/graphprompt/README.md |
| HGPrompt | scripts/hgprompt/README.md |
| MultiGPrompt | scripts/multigprompt/README.md |
| GCoT | scripts/gcot/README.md |
| Graver | scripts/graver/README.md |
| GraphMore | scripts/graphmore/README.md |
| Bridge | scripts/bridge/README.md |
| GraphKeeper | scripts/graphkeeper/README.md |
| GraphGPT | scripts/graphgpt/README.md |
| GraphText | scripts/graphtext/README.md |
| LLaGA | scripts/llaga/README.md |
| OneForAll | scripts/oneforall/README.md |
| RAG-GFM | scripts/rag_gfm/README.md |
Several baselines also ship an Experiment-Manual.md under scripts/<baseline>/ with step-by-step reproduction notes.
Versions below match pyproject.toml.
| Dependency | Version |
|---|---|
| Python | ≥ 3.12 |
| NumPy | ≥ 1.20 |
| PyYAML | ≥ 6 |
| Dependency | Version |
|---|---|
| PyTorch | 2.8.0 |
| PyTorch Geometric | 2.7.0 |
| torch-scatter | 2.1.2 |
| torch-sparse | 0.6.18 |
| torch-cluster | 1.6.3 |
| torch-spline-conv | 1.2.2 |
| Lightning | ≥ 2.2.0, < 3 |
| torchmetrics | ≥ 1.3.0 |
| Accelerate | ≥ 0.26.0 |
| OGB | 1.3.6 |
| geoopt | 0.5.1 |
| scikit-learn | ≥ 1.3.0 |
| Dependency | Version |
|---|---|
| Transformers | ≥ 4.36.0 |
| Hugging Face Hub | 1.10.2 |
| Hydra Core | 1.3.2 |
| OmegaConf | 2.3.0 |
| NumPy | 2.3.2 |
| Pandas | 3.0.2 |
| scikit-learn | 1.8.0 |
| Gradio | ≥ 5.0.0 |
| W&B | ≥ 0.19.0 |
| SwanLab | ≥ 0.7.11, < 0.8 |
| Dependency | Version |
|---|---|
| Transformers | 5.5.4 |
| Accelerate | 1.13.0 |
| PEFT | 0.19.0 |
| bitsandbytes | 0.49.2 |
| DeepSpeed | 0.18.9 |
| datasets | 4.8.4 |
| sentence-transformers | 5.4.1 |
See pyproject.toml for the full pinned dependency list.
This project is licensed under the Apache License 2.0.
MAGIC GROUP — Beihang University, School of Computer Science and Engineering, ACT Lab.