verodat-adri 6.1.0

Enterprise data quality layer for AI agents - Validates data quality with Verodat cloud integration. Requires Verodat API key.

ADRI - Agent Data Readiness Index

AI agents break on bad data. ADRI fixes that with one decorator.

from adri import adri_protected

@adri_protected(standard="customer_data", data_param="data")
def process_customers(data):
    # Your agent logic here
    return results

Auto-validates data quality. Works with any framework. 2 minutes to integrate.

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ADRI = Data Contracts for AI Agents

Think of ADRI standards as executable data contracts - formal agreements that define what good data looks like and automatically enforce those agreements at runtime.

Traditional approach: Documentation says "email must be valid" → agents break when it's not
ADRI approach: Contract defines email validation → invalid emails automatically blocked

# Your data contract (auto-generated from good data)
field_requirements:
  email:
    type: string
    nullable: false
    pattern: ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$
    validation_rules:
      - name: email format validation
        severity: CRITICAL
        rule_expression: REGEX_MATCH('...')

Contract benefits:

• 📋 Schema Contract: Field names and types must match
• ✅ Quality Contract: Validation rules must pass
• 📊 SLA Contract: Quality scores must meet thresholds
• 🔒 Enforcement: Violations blocked before reaching agents

This aligns ADRI with modern data architectures: Data Mesh (domain-owned contracts), Data Fabric (unified enforcement), DataOps (automated quality gates).

👉 Learn more about Data Contracts →

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How ADRI Works

flowchart LR
    A[Your Function Called] --> B[🛡️ ADRI Intercepts]
    B --> C{Quality Check<br/>5 Dimensions}
    C -->|Score ≥ 80| D[✅ ALLOW<br/>Function Runs]
    C -->|Score < 80| E[❌ BLOCK<br/>Error Raised]
    D --> F[📋 Log Results]
    E --> F

    style A fill:#e3f2fd,stroke:#2196f3,stroke-width:2px
    style B fill:#fff3e0,stroke:#ff9800,stroke-width:3px
    style C fill:#f3e5f5,stroke:#9c27b0,stroke-width:2px
    style D fill:#e8f5e9,stroke:#4caf50,stroke-width:2px
    style E fill:#ffebee,stroke:#f44336,stroke-width:2px
    style F fill:#fafafa,stroke:#757575,stroke-width:1px

In plain English: ADRI sits between your code and its data, checking quality before letting data through. Good data passes, bad data gets blocked.

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What is ADRI?

ADRI is the missing data layer for AI agents. It protects your AI agent workflows from bad data by:

• Auto-validating data quality across 5 dimensions (validity, completeness, consistency, accuracy, timeliness)
• Auto-generating quality standards on first successful run - no manual configuration
• Blocking or warning on quality failures based on your preference
• Logging insights locally for debugging and development

Framework agnostic: Works with LangChain, CrewAI, AutoGen, LlamaIndex, Haystack, Semantic Kernel, and any Python function.

Why ADRI?

AI agents are powerful, but fragile. One malformed field or missing value can crash your entire workflow. Traditional validation is tedious - you write dozens of if statements, manually check types, and hope you caught everything.

ADRI learns what good data looks like and enforces it automatically. Add one decorator, run with good data once, and you're protected.

Complete Example

from adri import adri_protected
import pandas as pd

@adri_protected(standard="customer_data", data_param="customer_data")
def analyze_customers(customer_data):
    """Your AI agent logic."""
    print(f"Analyzing {len(customer_data)} customers")
    return {"status": "complete"}

# First run with good data
customers = pd.DataFrame({
    "id": [1, 2, 3],
    "email": ["[e-mail]", "[e-mail]", "[e-mail]"],
    "signup_date": ["2024-01-01", "2024-01-02", "2024-01-03"]
})

analyze_customers(customers)  # ✅ Runs, auto-generates standard

What happened:

1. Function executed successfully
2. ADRI analyzed the data structure
3. Generated quality standard in ADRI/dev/standards/customer_data.yaml
4. Future runs validate against this standard

Future runs with bad data:

bad_customers = pd.DataFrame({
    "id": [1, 2, None],  # Missing ID
    "email": ["[e-mail]", "invalid-email", "[e-mail]"],  # Bad email
    # Missing signup_date column
})

analyze_customers(bad_customers)  # ❌ Raises exception with quality report

Installation

pip install adri

Requirements: Python 3.10+

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⭐ Star us if ADRI saves you time - it helps us grow!

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Quick Links

• Quickstart Guide - 2-minute integration guide
• Getting Started - Detailed 10-minute tutorial
• Standards Library - 13 production-ready standards to copy and use
• How It Works - Five quality dimensions explained
• Framework Patterns - LangChain, CrewAI, AutoGen examples
• CLI Reference - Command-line tools
• FAQ - Common questions
• Examples - Real-world examples

Features

🎯 One Decorator, Complete Protection

@adri_protected(standard="your_data", data_param="data")
def your_function(data):
    return results

🤖 Framework Agnostic

Works with any AI agent framework:

• LangChain & LangGraph
• CrewAI
• AutoGen
• LlamaIndex
• Haystack
• Semantic Kernel
• Generic Python

📊 Five Quality Dimensions

ADRI validates:

1. Validity - Data types and formats
2. Completeness - Required fields present
3. Consistency - Cross-field relationships
4. Accuracy - Value ranges and patterns
5. Timeliness - Data freshness

🔄 Auto-Generation

No manual configuration. ADRI learns from your data:

• Runs successfully with good data → generates standard
• Future runs → validates against standard
• Customize generated standards as needed

🛡️ Protection Modes

# Raise mode (default) - raises exception
@adri_protected(standard="data", data_param="data", on_failure="raise")

# Warn mode - logs warning, continues
@adri_protected(standard="data", data_param="data", on_failure="warn")

# Continue mode - silently continues
@adri_protected(standard="data", data_param="data", on_failure="continue")

🔧 CLI Tools

adri guide                                      # Interactive walkthrough (recommended for first-time users)
adri setup                                      # Initialize ADRI
adri generate-standard data.json                # Generate standard
adri assess data.csv --standard my_standard     # Assess data quality
adri list-standards                             # List standards
adri validate-standard my_standard.yaml         # Validate standard

📝 Local Logging

Developer-friendly insights during development:

• Quality scores and assessments
• Dimension-specific failures
• Auto-generated standards
• Stored in ADRI/dev/logs/

Common Use Cases

API Data Validation

@adri_protected(standard="api_response", data_param="response")
def process_api_data(response):
    return transform(response)

What it protects: API response data structure Sample data: examples/data/api_response.json Use when: Validating third-party API responses before processing

Multi-Agent Workflows

@adri_protected(standard="crew_context", data_param="context")
def crew_task(context):
    return crew.kickoff(context)

What it protects: Agent communication context Sample data: examples/data/crew_context.json Use when: Coordinating multi-agent workflows (CrewAI, AutoGen, custom)

RAG Pipelines

@adri_protected(standard="documents", data_param="docs")
def index_documents(docs):
    return index.insert(docs)

What it protects: Document structure before indexing Sample data: examples/data/rag_documents.json Use when: Validating documents before vector store indexing (LlamaIndex, Haystack)

Note: ADRI validates data structure, not content. For RAG, it ensures each document has required fields (id, text, metadata) and correct types, preventing indexing failures from malformed data.

Got a different use case? Share your story or contribute a standard - help the community!

📚 Don't Start from Scratch - Use Catalog Standards

13 battle-tested standards ready to copy and use - No need to write validation rules from scratch.

Business Domains

• 🎧 Customer Service - Support tickets, interactions
• 🛒 E-commerce Orders - Order processing, fulfillment
• 💰 Financial Transactions - Payments, accounting
• 🏥 Healthcare Patients - EHR systems, patient records
• 📊 Marketing Campaigns - Campaign tracking, ROI

AI Frameworks

• 🔗 LangChain Chains - Chain input validation
• 🤖 CrewAI Tasks - Multi-agent task context
• 📚 LlamaIndex Documents - RAG document structure
• 💬 AutoGen Messages - Agent message validation

Generic Templates

• 🌐 API Responses - REST API response structure
• ⏱️ Time Series - Metrics, sensor data
• 🔑 Key-Value Pairs - Configuration, settings
• 🌳 Nested JSON - Hierarchical structures

📖 Full Catalog | Can't find your use case? Add it! - Takes 15 minutes, helps everyone.

🤝 Share Your Standards

Built something with ADRI? Your standard could help hundreds of engineers.

1. Use ADRI on your data
2. Polish your standard
3. Submit a PR - Contribution guide

Why contribute?

• 🎯 Get featured in the catalog
• 💬 Connect with others in your domain
• 🚀 Help the community solve similar problems

Start Contributing | Discuss Ideas

Development

# Clone repository
git clone https://github.com/adri-standard/adri.git
cd adri

# Install in development mode
pip install -e .

# Run tests
pytest

# Run linters
flake8 src/
black src/

See CONTRIBUTING.md for contribution guidelines.

License

Apache 2.0 License. See LICENSE for details.

Community & Support

Get Help

• 💬 GitHub Discussions - Ask questions, share use cases
• 🐛 GitHub Issues - Report bugs, request features
• 📚 Documentation - Comprehensive guides and tutorials

Connect with the Team

• 👤 Thomas Russell - Founder (updates & engagement)
• 🦋 @thomas-ds.bsky.social - Real-time updates on Bluesky
• 🏢 Verodat - Company behind ADRI

Support This Project

If ADRI saves you time, show your support:

• ⭐ Star on GitHub - Helps us reach more developers
• 🗣️ Share your wins: "Solved [your use case] with #ADRI #AIAgents"
• 🤝 Contribute a standard: Your use case could help hundreds - Guide
• 💬 Discuss ideas: Propose new standards or share what you're building

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One decorator. Any framework. Reliable agents.

Built with ❤️ by Thomas Russell at Verodat