Model-agnostic XAI + RAI middleware layer for Python | Bitstek.io
Multi-modal Explainable and Responsible AI Python package — v0.1.5
A modular Python SDK for Explainable AI (XAI) and Responsible AI (RAI), combining model explainability, trust diagnostics, and multi-modal routing in a unified framework.
PyPI package name: xai-rai
Python SDK for modular explainable and responsible AI: model adapters, explainers, risk diagnostics, and multi-modal routing in one framework.
Developed and maintained by @8bitjawad
xai-rai provides a structured framework for building, testing, and extending explainability and responsible AI workflows.
Instead of using isolated tools for explanations, fairness checks, and robustness analysis, the SDK unifies them under a layered architecture.
It provides:
- Explainability algorithms (SHAP, LIME, counterfactuals, etc.)
- Responsible AI diagnostics (bias, drift, robustness)
- Pluggable model adapters for multiple modalities
- Routing and orchestration across components
- Visualization and reporting support
Supported modalities:
- Tabular models
- NLP
- Vision models
- LLMs
- Text-to-image (TTI) models
Libraries like SHAP or LIME solve explanation problems individually.
xai-rai aims to provide:
- A unified SDK abstraction
- Explainability and responsible AI in one system
- Multi-modal support via adapters
- Routing logic across models and modalities
- An extensible architecture for research and deployment
- LLM explanations for ease of understanding
xai-rai follows a layered, result-centric architecture:
Facade
↓
Pipeline
↓
Analyzers / Explainers
↓
Inference Engines / Adapters
↓
Unified Result Objects
↓
Charts / Reports / UI
- SHAP feature attribution
- LIME local explanations
- Counterfactual explanations
- Natural language narratives
- Multi-modal explanation pipelines
- Fairness diagnostics
- Population Stability Index (PSI) drift detection
- Robustness checks
- Confidence and anomaly scoring
pip install xai-raipip install xai-rai[tabular]
pip install xai-rai[tti]
pip install xai-rai[vision]
pip install xai-rai[nlp]
pip install xai-rai[llm]
pip install xai-rai[full]from PIL import Image
from xai_rai import TextToImageExplainer
explainer = TextToImageExplainer(
device="cpu",
enable_caption_analysis=True,
)
image = Image.open("generated.png")
result = explainer.explain(
image=image,
prompt="a futuristic cyberpunk city",
)
print(result.summary())See demo_tests/ and tti_demo.py for runnable examples.
from xai_rai import (
TabularExplainer,
VisionExplainer,
NLPExplainer,
LLMExplainer,
TextToImageExplainer,
)Each concern lives in its own layer.
Models are accessed through standardized interfaces.
Explainability and responsible AI diagnostics are distinct modules.
New modalities, explainers, diagnostics, and visualization layers can be added without changing the whole system.
- Tabular explainability
- NLP explainability
- Vision explainability
- LLM explainability
- Text-to-image explainability
- SHAP / LIME integration
- Counterfactual explanations
- Multi-modal result objects
- RAI diagnostics
- Trust and alignment analysis
xai-rai can support:
- Explainable healthcare models
- Explainable and responsible AI for businesses
- Responsible AI and toxicity screens
See CONTRIBUTING.md.
This project is in beta. If you wish to contribute:
- Keep modules modular and loosely coupled
- Follow the layered architecture
- Add type hints where possible
- Prefer result-centric APIs over raw dictionaries
- Include lightweight tests for new features
- Keep public APIs clean and stable
For major architectural changes or new modality integrations, open an issue or discussion first.
MIT — see LICENSE.