agentic-maya 0.1.0

Agentic Maya v1 Python CLI MVP

Agentic Maya

Agentic Maya — Governed multi-agent orchestration with YAML pipelines, policies, and skills for enterprise-grade AI workflows.

What It Does

Agentic Maya provides a secure, auditable framework for running multi-agent AI pipelines with built-in governance, memory management, and human oversight. It's designed for production use cases requiring compliance, traceability, and safety.

Key Features

• YAML-Driven Pipelines: Define complex workflows with steps, checkpoints, and fallback models
• Policy Enforcement: Six-layer governance system (Intent, Auth, Budget, PII, External Gate, Escalation)
• Skill Registry: Reusable, versioned agent capabilities with audit trails
• Memory Tiers: Session, episodic, long-term, and audit memory for safe persistence
• CLI Tools: Full lifecycle management with status, audit, checkpoint, and rollback commands
• HITL Integration: Human-in-the-loop approvals for critical decisions
• Audit Logging: Immutable, hash-chained logs for compliance

Quick Start

Installation

pip install agentic-maya
# or for development
pip install -e .

First Pipeline

# Run a sample pipeline
myna run ./pipelines/demo_pipeline.yml

# Check execution status
myna status <session_id>

# View detailed audit trail
myna audit <session_id>

CLI Commands

• myna run <pipeline.yml> - Execute a pipeline
• myna status <session_id> - Check pipeline status
• myna audit <session_id> - View audit logs
• myna checkpoint <session_id> --label <name> - Create checkpoint
• myna rollback <session_id> --to <checkpoint> - Rollback to checkpoint
• myna approve <session_id> --request-id <id> - Approve pending requests
• myna pause <session_id> - Pause execution
• myna terminate <session_id> - Stop pipeline

Architecture

Governance Layers

1. Intent Verification - Validate task intent against policies
2. Model Authorization - Check model access permissions
3. Budget Control - Enforce token and cost limits
4. PII Detection - Block sensitive data leaks
5. External Gate - Control API/tool usage
6. Escalation - Human approval for critical actions

Memory Tiers

• Session Memory: Current execution state
• Episodic Memory: Step checkpoints and resumes
• Long-Term Memory: Persistent knowledge and preferences
• Audit Memory: Compliance event history

Agent Roles

• Orchestrator: Controls pipeline execution
• Specialist: Executes domain tasks
• Subagent: Scoped helper agents
• Critic: Validates outputs and compliance
• Memory Manager: Handles all memory operations
• Tool Executor: Runs external tools/APIs
• Audit Agent: Enforces governance and logging

Documentation

📖 Full Documentation - Complete guides and API reference

• Core Concepts
• Pipeline Guide
• Policy Reference
• Skills Registry

Project Structure

agentic-myna/
├── main.py                 # CLI entry point
├── pyproject.toml          # Project configuration
├── myna.toml              # Runtime defaults
├── src/myna/              # Core package
│   ├── cli.py            # Command-line interface
│   ├── runtime.py        # Pipeline execution engine
│   ├── governance.py     # Policy enforcement
│   ├── memory.py         # Memory management
│   └── schemas.py        # Data validation
├── pipelines/             # YAML pipeline definitions
├── policies/              # YAML governance policies
├── skills/                # YAML skill definitions
├── tests/                 # Test suite
│   ├── test_*.py         # Unit tests
│   └── scenarios/        # Integration tests
├── docs/                  # Markdown documentation
└── website/               # Static website
    ├── index.html        # Homepage
    └── docs/             # HTML documentation

Development

Setup

git clone <repository-url>
cd agentic-myna
pip install -e .[dev]

Testing

# Run all tests
python -m pytest

# Run specific test
python -m pytest tests/test_governance.py -v

# Run with coverage
python -m pytest --cov=src/myna

Building Documentation

# Generate HTML docs from Markdown
python scripts/build_docs.py

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Write tests for new functionality
4. Ensure all tests pass (python -m pytest)
5. Commit changes (git commit -m 'Add amazing feature')
6. Push to branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Guidelines

• Follow PEP 8 style guidelines
• Add type hints for new functions
• Update documentation for API changes
• Ensure backward compatibility

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• 📧 Email: [e-mail]
• 🐛 GitHub: GitHub

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Agentic Maya - Safe, Auditable, Multi-Agent AI Orchestration