Generate EU AI Act Annex IV compliance documentation from your ML codebase
Project description
AIBOM
Generate EU AI Act Annex IV compliance documentation from your ML codebase in one command.
pip install aibom
aibom scan ./your-project
What it does
AIBOM scans your Python ML project and outputs a structured document listing:
- Datasets — HuggingFace datasets, TensorFlow datasets, PyTorch torchvision datasets
- Models — HuggingFace pretrained models, OpenAI models, Anthropic models
- External APIs — OpenAI, Anthropic, Google AI, Cohere, Replicate
This covers the dataset and model documentation requirements in EU AI Act Annex IV, Section 2.
Installation
pip install aibom
Requires Python 3.9+
Usage
# Scan a project, output JSON (default)
aibom scan ./my-project
# Save to file
aibom scan ./my-project --output report.json
# Markdown output
aibom scan ./my-project --format markdown --output report.md
Example output
{
"aibom_version": "0.1.0",
"generated_at": "2026-03-13T13:51:17Z",
"project_path": "./my-project",
"datasets": [
{
"name": "squad",
"source": "huggingface",
"source_url": "https://huggingface.co/datasets/squad",
"detected_in": "train.py:4"
}
],
"models": [
{
"name": "gpt-4",
"provider": "openai",
"model_card_url": null,
"detected_in": "inference.py:12"
}
],
"apis": [
{
"provider": "openai",
"endpoint_pattern": "chat.completions.create",
"detected_in": "inference.py:12"
}
]
}
What is the EU AI Act?
The EU AI Act requires providers of high-risk AI systems to maintain technical documentation including the datasets and models used. AIBOM generates the dataset and model inventory component of that documentation automatically.
Relevant articles: Annex IV Section 2, Article 11, Article 13.
Full enforcement for high-risk AI systems begins August 2027. Foundation model transparency rules are in effect since August 2025.
Detection coverage
| Source | Detected via |
|---|---|
| HuggingFace datasets | load_dataset() calls |
| HuggingFace models | from_pretrained() calls |
| OpenAI (new SDK) | client.chat.completions.create() |
| OpenAI (legacy) | openai.ChatCompletion.create() |
| Anthropic | client.messages.create() |
| TensorFlow datasets | tfds.load() calls |
| PyTorch torchvision | torchvision.datasets.* |
License
MIT
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file aibom_cli-0.1.0.tar.gz.
File metadata
- Download URL: aibom_cli-0.1.0.tar.gz
- Upload date:
- Size: 6.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
2e8fb2311269ea5240498a183eb5aa62c2b086904d0b3132b600f8502a3b1ca2
|
|
| MD5 |
c8a9d8eb5bb4b6fca93a56c0728923d9
|
|
| BLAKE2b-256 |
819767402538d8f9040f6273287e994f3d486a49752a289a6193f39f29ef0c33
|
File details
Details for the file aibom_cli-0.1.0-py3-none-any.whl.
File metadata
- Download URL: aibom_cli-0.1.0-py3-none-any.whl
- Upload date:
- Size: 7.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d41980d5353dfb9c01b1730d1c152a6b374b1c2e4dcbcb51e3f50a2e2c14f405
|
|
| MD5 |
846e9840973047dc5d752813d9dc529b
|
|
| BLAKE2b-256 |
93c9f8fb4f8768e09c2d009d24245dbbc7cf57ada369fa1268a747768381cc1c
|