AI Agentic Data Stack Framework - Community Edition

Open source data engineering and analytics framework with interactive AI agents, comprehensive templates, and complete example projects.

🚀 Quick Start

# Install globally
npm install -g agentic-data-stack-community

# Try the complete example project first
cd examples/simple-ecommerce-analytics
python sample-data/generate-sample-data.py

# Activate interactive agents
agentic-data agent data-analyst
*analyze-data

# Or run structured workflows
agentic-data workflow community-analytics-workflow

# Create your own project
agentic-data init my-analytics-project

🌟 What's Included

🤖 4 Interactive AI Agents

• Data Engineer (Emma ⚙️): Pipeline development, ETL processes, infrastructure setup
• Data Analyst (Riley 📈): Customer segmentation, RFM analysis, business insights
• Data Product Manager (Morgan 📊): Requirements gathering, stakeholder coordination
• Data Quality Engineer (Quinn 🔍): 3-dimensional quality validation and monitoring

📋 20 Essential Templates

• Data Contracts: Customer data, order processing, product catalogs
• Implementation: SQL analysis, Python validation scripts
• Project Setup: Business requirements, architecture planning
• Quality Validation: Automated testing and monitoring
• Documentation: User guides, technical specifications

🔍 3-Dimensional Quality Framework

• Completeness: Data availability and coverage validation
• Accuracy: Format checking and type validation
• Consistency: Cross-reference validation and uniqueness checks

🎯 Interactive Agent System

• Agent Activation: @data-analyst for guided assistance
• Command Execution: *analyze-data for task-specific operations
• Interactive Shell: agentic-data interactive for persistent agent sessions
• Multi-Agent Workflows: Advanced orchestration with context handoffs
• Progressive Disclosure: 12+ elicitation methods for quality content creation
• Session Persistence: Workflow continuity and progress tracking

📊 Complete E-commerce Example

• Customer segmentation with RFM analysis
• Data quality validation scripts
• Business requirements documentation
• Sample data generation tools
• Interactive agent walkthroughs

📦 Installation

Global Installation (Recommended)

npm install -g agentic-data-stack-community

Local Project Installation

npm install agentic-data-stack-community
npx agentic-data init my-project

Development Installation

git clone https://github.com/barnyp/agentic-data-stack-framework-community
cd agentic-data-stack-framework-community
npm install
npm link  # Make CLI available globally

🛠️ CLI Commands

# Framework Information
agentic-data info                    # Display framework overview
agentic-data --version               # Show version

# Interactive Shell (Recommended)
agentic-data interactive             # Enter interactive shell mode

# Interactive Agents
agentic-data agent <agent-name>      # Activate interactive agent (legacy)
agentic-data agents list             # List available agents
agentic-data agents show <agent>     # Show agent details

# Workflows and Tasks
agentic-data workflow <workflow-name> # Execute structured workflow
agentic-data task <task-name>        # Execute specific task

# Templates and Examples
agentic-data templates list         # List available templates
agentic-data templates show <template> # Show template details
agentic-data examples list          # List available examples

# Project Management  
agentic-data init [project-name]     # Create new project
agentic-data validate               # Run quality validation

🐚 Interactive Shell Mode

The interactive shell provides a persistent, conversational interface with AI agents:

# Enter interactive mode
agentic-data interactive

# Inside the shell:
@data-analyst                        # Activate Data Analyst agent
*help                               # Show agent capabilities
*task                               # List available tasks
*analyze-data                       # Execute data analysis task
*create-doc analysis-report         # Create document from template
*exit                               # Deactivate current agent
exit                                # Exit interactive shell

Interactive Commands

• Agent Activation: @data-engineer, @data-analyst, @data-product-manager, @data-quality-engineer
• Task Commands: *task <name>, *analyze-data, *create-dashboard, *define-metrics
• Document Commands: *create-doc <template>, *shard-doc <path>, *manage-docs
• Knowledge Commands: *kb-mode, *search <query>
• Expansion Commands: *manage-packs, *install-pack <name>, *create-pack

🏗️ Framework Architecture

AI Agentic Data Stack Framework - Community Edition
├── 🤖 Interactive AI Agents (4)
│   ├── Data Engineer (Emma ⚙️)
│   ├── Data Analyst (Riley 📈)  
│   ├── Data Product Manager (Morgan 📊)
│   └── Data Quality Engineer (Quinn 🔍)
├── 📋 Templates & Tasks (30)
│   ├── Templates (20): Data contracts, analysis, dashboards
│   ├── Tasks (10): Pipeline building, analysis, quality checks
│   └── Checklists (8): Quality validation, deployment
├── 🔄 Workflows (9)
│   ├── Brownfield (5): System integration workflows
│   └── Greenfield (4): New project workflows
├── 🔍 Quality Framework
│   ├── Completeness Validation
│   ├── Accuracy Checking
│   └── Consistency Verification
└── 📚 Complete Examples
    ├── E-commerce Analytics (SQL + Python)
    ├── Interactive CLI Interface
    └── Sample Data Generation

🎯 Use Cases

Customer Analytics

• RFM Segmentation: Recency, Frequency, Monetary analysis
• Customer Journey: Lifecycle and behavior tracking
• Marketing Optimization: Targeted campaign development

Data Quality Management

• Automated Validation: 3-dimensional quality checks
• Data Monitoring: Continuous quality tracking
• Issue Detection: Format and consistency validation

Business Intelligence

• Reporting: Automated insight generation
• Dashboard Development: Self-service analytics
• Performance Tracking: KPI monitoring and alerts

📊 Complete Example: E-commerce Customer Segmentation

1. Try the Built-in Example

# Navigate to the included example
cd examples/simple-ecommerce-analytics

# Generate realistic sample data  
python sample-data/generate-sample-data.py

2. Use Interactive Shell Mode

# Enter interactive mode (recommended)
agentic-data interactive

# Start with requirements gathering
@data-product-manager
*gather-requirements
*exit

# Perform data analysis  
@data-analyst
*analyze-data
*segment-customers
*exit

# Validate data quality
@data-quality-engineer
*implement-quality-checks
*exit

# Exit interactive shell
exit

3. Or Use Structured Workflows

# Execute the complete workflow with agent handoffs
agentic-data workflow community-analytics-workflow
# Follow the interactive prompts for each step

Expected Results

• 5-7 Customer Segments: Champions, Loyal Customers, At Risk, etc.
• 90%+ Data Quality: Across completeness, accuracy, consistency
• Marketing Ready Lists: Exportable customer segments with campaign recommendations

🔧 Configuration

Project Structure

my-project/
├── data-contracts/          # Data specifications
├── implementation/          # SQL scripts & Python code
├── documentation/           # Project documentation  
├── validation/             # Quality validation scripts
├── sample-data/            # Test data and generators
└── README.md               # Project overview

Data Contracts Example

# customer-data-contract.yaml
contract_metadata:
  name: "customer_data_contract_community"
  framework_version: "AI Agentic Data Stack Community v1.0"

business_context:
  objective: "Customer segmentation for targeted marketing"
  
quality_framework:
  dimensions:
    completeness:
      customer_id: {threshold: 100.0, criticality: "critical"}
      email: {threshold: 95.0, criticality: "high"}
    accuracy:
      email_format: {threshold: 95.0, validation: "regex_email"}
    consistency:
      customer_id_unique: {threshold: 100.0, check: "uniqueness"}

🚀 Getting Started Tutorial

Step 1: Install and Try Example

npm install -g agentic-data-stack-community

# Start with the complete example (recommended)
cd examples/simple-ecommerce-analytics
python sample-data/generate-sample-data.py

Step 2: Explore Interactive Shell

# See what's available
agentic-data info
agentic-data agents list

# Enter interactive shell mode
agentic-data interactive

# Activate your first agent
@data-analyst
*help
*task
*analyze-data
*exit

# Exit shell
exit

Step 3: Try Workflows

# Execute structured multi-agent workflows
agentic-data workflow community-analytics-workflow
# Follow the interactive prompts for each step

Step 4: Create Your Own Project

# Initialize your own project
agentic-data init my-analytics-project
cd my-analytics-project

# Copy patterns from the example
cp -r ../examples/simple-ecommerce-analytics/implementation .

Step 5: Interactive Shell

# Enter persistent interactive mode
agentic-data interactive
# Try different agents and commands

📈 Performance and Scale

Community Edition Capabilities

• Data Volume: Up to 1M records per analysis
• Processing: Single-machine processing optimized
• Quality Checks: 3-dimensional framework
• Export Formats: CSV, JSON for marketing tools
• Update Frequency: Daily batch processing

Performance Benchmarks

• Segmentation Analysis: ~30 seconds for 100K customers
• Quality Validation: ~15 seconds for 500K records
• Data Export: ~5 seconds for 50K customer lists

🤝 Community & Support

Community Resources

• GitHub Discussions: Ask questions, share insights
• Documentation: Complete guides and tutorials
• Examples: Real-world implementations
• Contributing: Help improve the framework

Getting Help

1. Check Documentation: Start with README and examples
2. Search Issues: Look for similar questions on GitHub
3. Ask Community: Post in GitHub Discussions
4. Report Bugs: Create detailed issue reports

Contributing Guidelines

We welcome contributions! Please see CONTRIBUTING.md for:

• Code contribution process
• Documentation improvements
• Example submissions
• Bug reporting guidelines

🏢 Enterprise Edition

Ready for advanced features? Enterprise Edition includes:

Additional Capabilities

• 8 Specialized Agents: Including Data Scientist, Governance Officer, Experience Designer
• 88 Interactive Templates: Industry-specific solutions and advanced patterns
• 7-Dimensional Quality: ML-enhanced validation with predictive analytics
• Real-time Collaboration: Multi-user workflows and approval processes
• Advanced Compliance: HIPAA, GDPR, SOX automation
• Professional Support: Training, consulting, and technical support

Industry Solutions

• Healthcare: HIPAA-compliant patient analytics
• Financial Services: Risk modeling and compliance
• Retail: Advanced recommendation engines
• Manufacturing: Supply chain optimization

Contact Enterprise

📞 Sales: [email protected]
🌐 Website: Enterprise Features
📅 Demo: Schedule a personalized demonstration

📄 License & Legal

Community Edition License

This Community Edition is licensed under the MIT License.

Comparison

| Feature | Community Edition | Enterprise Edition | |---------|------------------|-------------------| | AI Agents | 4 Core Agents | 8 Specialized Agents | | Templates | 20 Essential | 88 Interactive | | Quality Framework | 3-Dimensional | 7-Dimensional + ML | | Support | Community | Professional | | License | MIT (Open Source) | Commercial | | Compliance | Basic | Advanced (HIPAA, GDPR) |

🚀 Ready to transform your data operations? Start with cd examples/simple-ecommerce-analytics and explore interactive agents!

Framework: AI Agentic Data Stack - Community Edition v1.1.2
License: MIT
Community: GitHub Discussions
Enterprise: [email protected]