auditai
EU AI Act Deployer Compliance SDK — wrap Claude, GPT, Ollama or any OpenAI-compatible LLM and generate Article 26 reports in minutes.
Install
pip install auditai-sdk
Quickstart
from auditai import wrap_client
import anthropic
client = wrap_client(anthropic.Anthropic(), project="my-app")
# Your code stays identical — every call is now logged and risk-classified
response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=1024,
messages=[{"role": "user", "content": "Hello"}],
)
Supported providers
Works with any OpenAI-compatible API — including local LLMs:
from openai import OpenAI
from auditai import wrap_client
# OpenAI
client = wrap_client(OpenAI(), project="my-app")
# Ollama (local)
client = wrap_client(
OpenAI(base_url="http://localhost:11434/v1", api_key="ollama"),
project="my-app"
)
# LM Studio (local)
client = wrap_client(
OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio"),
project="my-app"
)
# vLLM, llama.cpp, Azure OpenAI — same pattern
CLI
# Risk classification wizard (9 questions → EU AI Act category)
auditai classify
# View call stats
auditai stats --project my-app
# Generate Article 26 Deployer Report (PDF)
auditai report --project my-app --company "Acme SL" --email "cto@acme.com"
# Launch Streamlit dashboard
auditai dashboard --project my-app
What gets logged
Every AI call is recorded in a JSONL audit trail:
{
"call_id": "uuid",
"timestamp": "2026-05-07T20:00:00Z",
"provider": "anthropic",
"model": "claude-haiku-4-5-20251001",
"input_tokens": 312,
"output_tokens": 87,
"input_hash": "sha256...",
"output_preview": "first 100 chars...",
"risk_category": "limited",
"hitl_required": false
}
Generate compliance report
from auditai import generate_report
report_path = generate_report(
project="my-app",
company_name="Acme SL",
contact_email="compliance@acme.com",
extra_info={
"system_description": "Customer support chatbot",
"use_case": "Automated responses to user queries",
},
)
# → EU_AI_Act_Report_my-app_2026-05-07.pdf
The report covers Art. 26 obligations: risk classification, technical evidence, HITL events, and deployer declaration.
Links
- Website: auditaisdk.com
- PyPI: pypi.org/project/auditai-sdk
- Contact: marc@auditaisdk.com
Metadata
Release files for auditai-sdk 0.2.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| auditai_sdk-0.2.6.tar.gz | 28.9 kB | Without provenance | Details for auditai_sdk-0.2.6.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| auditai_sdk-0.2.6-py3-none-any.whl | Python 3 | none | any | Details for auditai_sdk-0.2.6-py3-none-any.whl |
Total release size: 59.1 kB
Release files / auditai_sdk-0.2.6.tar.gz
| Download URL | auditai_sdk-0.2.6.tar.gz |
|---|---|
| Size | 28.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.14.4
|
Release files / auditai_sdk-0.2.6-py3-none-any.whl
| Download URL | auditai_sdk-0.2.6-py3-none-any.whl |
|---|---|
| Size | 30.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.14.4
|
Security for auditai-sdk 0.2.6
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No provenance found
None of the files in this release were uploaded with provenance metadata.
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