aria-cli 1.0.2

ARIA CLI - Natural Language Quantum AI Command Line Interface with Q0-Q38 System

ARIA CLI v0.2.2

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Advanced Recursive Intelligence Architecture

---

Natural Language Quantum AI Command Line Interface with full Q0-Q38 Cognitive Architecture

✨ What's New in v0.2.1

• 🤖 ARIA Agents - Autonomous agents with Q-System integration
• 📡 Agent-to-Agent Communication - Multi-agent messaging, channels, and swarms
• 🐝 Agent Swarm - Coordinated multi-agent task execution

✨ What's New in v0.2.0

• 39 Cognitive Layers (Q0-Q38) - Four domains: Foundation, Reasoning, Metacognition, Transcendence
• Multi-backend LLM Integration - OpenAI, Anthropic, Ollama, llama.cpp, Transformers
• Q38 Cluster Connector - Native integration with 64-direction distributed processing
• CLI-GUI Sync - Real-time synchronization between CLI and GUI interfaces
• Plugin System - Extensible architecture for custom functionality
• Rich Terminal UI - Beautiful output with typer + rich
• 🔐 Authentication System - Secure login with local, API key, and SSH key auth
• 🔗 SSH Router - Multi-host SSH management, tunnels, and remote execution

📦 Installation

From PyPI (Recommended)

pip install aria-cli

With LLM Support

pip install aria-cli[openai]      # OpenAI backend
pip install aria-cli[anthropic]   # Anthropic backend
pip install aria-cli[local]       # Local models (llama.cpp, transformers)
pip install aria-cli[full]        # All backends + rich UI

From npm (Node.js wrapper)

npm install -g @alphamatt/aria-cli

From Source

git clone https://github.com/universal-crown-prime/aria-cli.git
cd aria-cli
pip install -e .

🚀 Quick Start

# Ask a question
aria ask "What is quantum computing?"

# Search for information
aria search "trading systems"

# Show Q-System layers
aria layers

# Show system status
aria status

# Enter interactive chat mode
aria chat

# Remember something
aria remember "Meeting at 3pm tomorrow" --key meeting

# Recall memories
aria recall --key meeting

# Login
aria login

# SSH to a host
aria ssh connect production

# Create and manage agents
aria agent create my-assistant --type reasoning
aria agent list
aria agent send my-assistant "Analyze this data"

🤖 ARIA Agents

Autonomous AI agents with Q-System integration for distributed processing.

Agent Types

Type	Q-Layer	Description
task	Q23	Task execution (shell, files)
reasoning	Q10	Logical analysis and inference
coordinator	Q21	Multi-agent coordination
generator	Q19	Content and code generation

CLI Commands

# Create an agent
aria agent create my-agent --type task --start

# List all agents
aria agent list

# Get agent status
aria agent status my-agent

# Send message to agent
aria agent send my-agent "Process this request" --priority HIGH

# Start/stop agent
aria agent start my-agent
aria agent stop my-agent

# View agent types and capabilities
aria agent types
aria agent capabilities

Agent Channels

# Create communication channel
aria agent channel create task-channel --type direct --agent-a agent1 --agent-b agent2

# List channels
aria agent channel list

Agent Swarm

# Create a coordinated swarm
aria agent swarm create my-swarm

Python API

from aria_cli import (
    AriaAgent, AgentRegistry, AgentSwarm,
    create_agent, get_registry,
    TaskAgent, ReasoningAgent, CoordinatorAgent,
    AgentMessage, MessageType, AgentCapability,
)
import asyncio

# Create agents
task_agent = create_agent("task", "file-processor")
reasoning_agent = create_agent("reasoning", "analyzer")

# Register with global registry
registry = get_registry()
asyncio.run(registry.register(task_agent))
asyncio.run(registry.register(reasoning_agent))

# Start agents
asyncio.run(task_agent.start())
asyncio.run(reasoning_agent.start())

# Send message between agents
message = AgentMessage(
    type=MessageType.REQUEST,
    sender=task_agent.id,
    recipient=reasoning_agent.id,
    payload={"action": "analyze", "data": {...}}
)
asyncio.run(reasoning_agent.receive(message))

# Create a swarm for coordinated tasks
swarm = AgentSwarm("processing-swarm")
swarm.add_agent(task_agent)
swarm.add_agent(reasoning_agent)

asyncio.run(swarm.initialize())

# Execute complex task
result = asyncio.run(swarm.execute({
    "goal": "Process and analyze data",
    "data": {...}
}))

Custom Agents

from aria_cli import AriaAgent, QLayer, AgentCapability, AgentMessage
from typing import Optional

class MyCustomAgent(AriaAgent):
    def __init__(self):
        super().__init__(
            name="my-custom-agent",
            q_layer=QLayer.Q15_CAUSATION,
            capabilities=[
                AgentCapability.ANALYSIS,
                AgentCapability.REASONING,
            ]
        )
    
    async def process_message(self, message: AgentMessage) -> Optional[AgentMessage]:
        action = message.payload.get("action")
        
        if action == "analyze_causation":
            result = await self.analyze_causes(message.payload.get("data"))
            return message.reply({"causes": result})
        
        return message.reply({"error": "Unknown action"})
    
    async def analyze_causes(self, data):
        # Your custom logic here
        return {"primary_cause": "...", "contributing_factors": [...]}

Agent Communication Channels

from aria_cli import (
    ChannelManager, DirectChannel, BroadcastChannel,
    StreamChannel, get_channel_manager,
)

# Get channel manager
manager = get_channel_manager()

# Create direct channel between two agents
direct = manager.create_direct("task-comm", agent_a, agent_b)
asyncio.run(direct.open())

# Create broadcast channel for events
broadcast = manager.create_broadcast("events", publisher_agent)
broadcast.subscribe(subscriber1.id)
broadcast.subscribe(subscriber2.id)
asyncio.run(broadcast.open())

# Broadcast an event
asyncio.run(broadcast.publish({"event": "task_complete", "result": {...}}))

# Create streaming channel for continuous data
stream = manager.create_stream("data-stream", producer, consumer)
asyncio.run(stream.open())

# Stream data
for chunk in data_generator():
    asyncio.run(stream.stream(chunk))
asyncio.run(stream.end_stream())

🔐 Authentication

ARIA CLI includes a secure authentication system for managing credentials and sessions.

Login Commands

# Interactive login
aria login

# Login with API key
aria login --api-key sk-... --provider openai

# Login with SSH key
aria login --ssh-key ~/.ssh/id_rsa

# Check current user
aria whoami

# Logout
aria logout

Python API

from aria_cli import AuthManager, get_auth

# Get auth manager
auth = get_auth()

# Register a new user
auth.register("username", "password", email="[e-mail]")

# Login
session = auth.login("username", "password")
print(f"Logged in as {session.user.username}")

# Check authentication
if auth.is_authenticated:
    print(f"Welcome, {auth.current_user.username}")

# Login with API key
session = auth.login_with_api_key("sk-...", provider="openai")

# Logout
auth.logout()

Credential Storage

Credentials are stored securely using:

• System keyring (when available)
• Encrypted file storage (fallback)

~/.aria/
├── session.json         # Current session (encrypted token)
├── .credentials         # Encrypted credential storage
└── .key                 # Encryption key (restricted permissions)

🔗 SSH Router

Manage SSH connections, execute remote commands, and create tunnels.

CLI Commands

# Add a host
aria ssh add production --hostname prod.example.com --user deploy --key ~/.ssh/id_rsa

# List hosts
aria ssh list

# Test connection
aria ssh test production

# Connect interactively
aria ssh connect production

# Execute remote command
aria ssh exec production "ls -la /var/www"

# Execute on multiple hosts
aria ssh exec production,staging "uptime"

# Create tunnel (local:8080 → remote:80)
aria ssh tunnel production --local 8080 --remote 80

# Upload file
aria ssh upload production ./local-file.txt /remote/path/

# Download file
aria ssh download production /remote/file.txt ./local-path/

Python API

from aria_cli import SSHRouter, SSHHost, get_router
from pathlib import Path

# Get router
router = get_router()

# Add a host
router.add_host(SSHHost(
    name="production",
    hostname="prod.example.com",
    username="deploy",
    key_file=Path("~/.ssh/id_rsa").expanduser(),
    port=22,
    tags=["prod", "web"],
))

# List hosts
hosts = router.list_hosts()
for host in hosts:
    print(f"{host.name}: {host.hostname}")

# Test connection
success, message = router.test_connection("production")
print(f"Connection: {message}")

# Execute command
result = router.execute("production", "ls -la")
print(result.stdout)
print(f"Exit code: {result.exit_code}")

# Execute on multiple hosts in parallel
results = router.execute_multi(
    ["production", "staging"],
    "uptime",
    parallel=True
)
for host, result in results.items():
    print(f"{host}: {result.stdout}")

# Create SSH tunnel
router.create_tunnel(
    "production",
    local_port=8080,
    remote_port=80,
)

# Create reverse tunnel
router.create_reverse_tunnel(
    "production",
    remote_port=9000,
    local_port=3000,
)

# Upload file
router.upload("production", "./local-file.txt", "/remote/path/")

# Download file
router.download("production", "/remote/file.txt", "./local-path/")

# Interactive session
router.connect_interactive("production")

# Close tunnels
router.close_tunnel("production", 8080)
router.disconnect_all()

SSH Configuration

The router automatically loads hosts from ~/.ssh/config:

# ~/.ssh/config
Host production
    HostName prod.example.com
    User deploy
    IdentityFile ~/.ssh/id_rsa
    Port 22

Host staging
    HostName staging.example.com
    User deploy
    ProxyJump bastion

Additional hosts can be stored in ~/.aria/ssh/hosts.json.

📱 AriaSpace - Phone ↔ Computer Sync

AriaSpace is a personal codespace system for securely syncing files between your phone (Android/Termux) and computer. Think GitHub Codespaces, but for your personal devices.

Architecture

    Phone (Termux)                    Computer
    ┌─────────────────┐              ┌─────────────────┐
    │  AriaSpace      │    SSH       │  AriaSpace      │
    │  Client         │◄────────────►│  Server         │
    │                 │              │                 │
    │  /storage/      │   Sync       │  ~/aria-space/  │
    │  emulated/0/    │◄────────────►│  workspaces/    │
    │  Meta AI/       │              │                 │
    └─────────────────┘              └─────────────────┘

Termux Setup (On Android)

1. Install Termux from F-Droid
2. Run the ARIA setup script:

# Get the setup script
aria termux setup-script | bash

# Or manually:
pkg update && pkg install openssh -y
termux-setup-storage
sshd

3. Note your IP address: ip addr show wlan0

Connect from Computer

# Discover devices on network
aria termux discover

# Or add device manually
aria termux add-host termux-phone [phone] --port 8022

# Test connection
aria termux test termux-phone

# Find Meta AI folder
aria termux find-meta-ai termux-phone

# List Android folders
aria termux folders termux-phone

Create Workspace

# Quick setup for Meta AI folder
aria space create-meta-ai termux-phone

# Or create custom workspace
aria space create my-docs termux-phone "/storage/emulated/0/Documents" \
    --local ~/aria-space/my-docs

# List workspaces
aria space list

Sync Files

# Bidirectional sync
aria space sync meta-ai

# Download only (phone → computer)
aria space pull meta-ai

# Upload only (computer → phone)
aria space push meta-ai

# Preview changes without syncing
aria space sync meta-ai --dry-run

Browse & Manage

# List files with status
aria space files meta-ai

# Browse remote directory
aria space browse meta-ai --path images

# Check workspace status
aria space status meta-ai

Python API

from aria_cli import AriaSpace, TermuxSetup, get_space

# Setup Termux connection
setup = TermuxSetup()
setup.wizard()  # Interactive setup

# Or programmatically
setup.append_ssh_config("termux-phone", "192.168.1.100", port=8022)

# Create workspace
space = get_space()
ws = space.create_workspace(
    name="meta-ai",
    local_root="~/aria-space/meta-ai",
    remote_root="/storage/emulated/0/Meta AI",
    remote_host="termux-phone",
)

# List files
files = space.list_files("meta-ai")
for f in files:
    print(f"{f.status.value}: {f.relative_path}")

# Sync workspace
result = space.sync("meta-ai")
print(f"Uploaded: {result.files_uploaded}, Downloaded: {result.files_downloaded}")

# Download specific file
space.download_file("meta-ai", "images/photo.jpg")

# Upload file
space.upload_file("meta-ai", "notes.txt")

# Browse remote
entries = space.browse_remote("meta-ai", "images")
for entry in entries:
    print(f"{'📁' if entry['is_dir'] else '📄'} {entry['name']}")

# Watch for changes (auto-sync)
space.watch("meta-ai", interval=60)

Supported Android Folders

Folder	Path
Meta AI	/storage/emulated/0/Meta AI
Downloads	/storage/emulated/0/Download
Documents	/storage/emulated/0/Documents
Pictures	/storage/emulated/0/Pictures
DCIM	/storage/emulated/0/DCIM
Movies	/storage/emulated/0/Movies
Music	/storage/emulated/0/Music
Termux Home	/data/data/com.termux/files/home

Storage Structure

~/.aria/
├── spaces/
│   ├── workspaces.json      # Workspace configurations
│   ├── state/               # Sync state per workspace
│   │   ├── meta-ai.json
│   │   └── ...
│   └── snapshots/           # Workspace snapshots
│       └── meta-ai/
│           └── 20241222_143052.json
└── termux/
    ├── devices.json         # Discovered devices
    └── aria_termux_setup.sh # Setup script

🧠 Q-System Architecture

The Q-System is a 39-layer recursive cognitive architecture:

Configuration

ARIA CLI stores configuration in ~/.aria/:

~/.aria/
├── config.json          # CLI configuration
├── sync_state.json      # CLI-GUI sync state
└── q-memory/            # Q-System memory storage
    ├── memory_*.json    # Saved memories
    └── ...

Configuration Options

Edit ~/.aria/config.json:

{
  "llm": {
    "backend": "openai",
    "model": "gpt-4o-mini",
    "temperature": 0.7
  },
  "q_system": {
    "default_layer": 8,
    "trace_enabled": true
  },
  "ui": {
    "theme": "dark",
    "show_layer_info": true
  }
}

Environment Variables

# LLM backends
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
export OLLAMA_HOST="http://localhost:11434"

# Q38 Cluster
export Q38_API_URL="http://localhost:8080"
export Q38_SYNC_PORT="9000"

🧩 Q-System Layers

Foundation (Q0-Q9)

Layer	Name	Description
Q0	VOID	The null state - pure potential
Q1	PERCEPTION	Raw sensory input processing
Q2	ATTENTION	Focus and salience detection
Q3	PATTERN	Pattern recognition
Q4	MEMORY_SHORT	Working memory
Q5	MEMORY_LONG	Long-term storage
Q6	ASSOCIATION	Concept linking
Q7	CONTEXT	Contextual understanding
Q8	LANGUAGE	Linguistic processing
Q9	EMOTION	Affective processing

Reasoning (Q10-Q19)

Layer	Name	Description
Q10	LOGIC	Formal logical reasoning
Q11	INFERENCE	Drawing conclusions
Q12	HYPOTHESIS	Generating hypotheses
Q13	ABSTRACTION	Abstract concept formation
Q14	ANALOGY	Analogical reasoning
Q15	CAUSATION	Causal reasoning
Q16	PREDICTION	Future projection
Q17	EVALUATION	Assessment
Q18	SYNTHESIS	Combining ideas
Q19	CREATIVITY	Novel generation

Action (Q20-Q29)

Layer	Name	Description
Q20	INTENTION	Goal setting
Q21	PLANNING	Strategy formation
Q22	DECISION	Choice making
Q23	EXECUTION	Action taking
Q24	MONITORING	Progress tracking
Q25	FEEDBACK	Loop processing
Q26	CORRECTION	Error correction
Q27	OPTIMIZATION	Performance tuning
Q28	LEARNING	Knowledge acquisition
Q29	ADAPTATION	Behavioral adjustment

Transcendence (Q30-Q38)

Layer	Name	Description
Q30	AWARENESS	Self-awareness
Q31	REFLECTION	Self-reflection
Q32	METACOGNITION	Thinking about thinking
Q33	IDENTITY	Self-identity modeling
Q34	VALUES	Value system and ethics
Q35	WISDOM	Applied knowledge
Q36	INTEGRATION	Holistic processing
Q37	EMERGENCE	Emergent properties
Q38	TRANSCENDENCE	Beyond individual layers

🔌 Python API

from aria_cli import QSystem, QLayer, QCommand, QOperator

# Initialize Q-System
q = QSystem()

# Create and execute a command
cmd = QCommand(
    operator=QOperator.ASK,
    payload={"question": "What is consciousness?"},
    layer=QLayer.Q30_AWARENESS,
)
result = q.execute(cmd)
print(result)

With LLM Integration

from aria_cli import QSystem, LLMConnector, LLMProvider

# Initialize with OpenAI
q = QSystem()
llm = LLMConnector(LLMProvider.OPENAI)

# Generate with Q-System context
response = llm.generate(
    "Explain quantum consciousness",
    system="You are ARIA at Q-Layer 38"
)
print(response)

NLP Processing

from aria_cli import NLPEngine

nlp = NLPEngine()

# Parse natural language
parsed = nlp.parse("Search for files containing quantum algorithms")
print(f"Intent: {parsed.intent}")       # Intent.SEARCH
print(f"Keywords: {parsed.keywords}")   # ['files', 'quantum', 'algorithms']
print(f"Q-Layer: Q{parsed.q_layer}")    # Q3 (PATTERN)

CLI-GUI Synchronization

from aria_cli import AriaConnector, SyncMode

# File-based sync
connector = AriaConnector(mode=SyncMode.FILE, source='cli')
connector.start()

# Update state
connector.update_state(current_layer=30)

# Stop sync
connector.stop()

🔄 Migration from v0.1.x

See MIGRATION.md for detailed upgrade instructions.

Key Changes

Feature	v0.1.x	v0.2.0
Q-System Layers	8	39 (Q0-Q38)
LLM Integration	Optional	Multi-backend
Package Structure	aria_cli/	src/aria_cli/
CLI Framework	argparse	typer + rich
Async Support	❌	✅
GUI Sync	❌	✅

📚 Commands Reference

Command	Description
aria ask <question>	Ask a natural language question
aria search <query>	Search for information
aria chat	Enter interactive chat mode
aria layers	Show all 39 Q-System layers
aria status	Show system status
aria remember <content>	Save to memory
aria recall	List/retrieve memories
aria help	Show help

🔧 Development

Setup

git clone https://github.com/universal-crown-prime/aria-cli.git
cd aria-cli
pip install -e ".[dev]"

Testing

pytest

Building

pip install build
python -m build

📄 License

MIT License - see LICENSE for details.

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

📬 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Read the Docs