Agentic AI

🔥 AGENTIC AI — Commercial Edition v4.0

"The mycellium must be conscious of itself."

Build production-grade autonomous AI agents with the framework stack trusted by enterprises. From persistent multi-agent councils to secure memory architectures—deploy with confidence.

🏆 2026 Research Coverage: 6 parallel deep dives • 250k+ tokens analyzed • Feb 2026 cutting edge

⚡ Quick Start

# 1. Install (60 seconds)
npx clawhub@latest install agentic-ai

# 2. Verify installation
clawhub doctor

# 3. Run your first agent
cd examples && python3 hello_agent.py

That's it. Your first agent is running with:

• ✅ 4-tier model fallback (always-on resilience)
• ✅ Persistent memory (cross-session continuity)
• ✅ Dharmic security gates (17 ethical checkpoints)

📋 Table of Contents

• Quick Start
• Installation
• Commercial Features
• Pricing
• Support
• Core Architecture
• Framework Deep Dive
• Memory Systems
• Protocols & Standards
• Security
• Self-Improvement
• API Reference
• Troubleshooting

🚀 Installation

Prerequisites

• Python 3.10+
• Node.js 18+ (for clawhub CLI)
• 4GB RAM minimum (8GB recommended)

Option 1: One-Command Install (Recommended)

npx clawhub@latest install agentic-ai

This installs:

• Core skill files
• Example projects
• Test suite
• Configuration templates

Option 2: Manual Install

# Clone repository
git clone https://github.com/dgclabs/agentic-ai.git
cd agentic-ai

# Install dependencies
pip install -r requirements.txt

# Run setup script
python3 install.py

Verification

# Run integration test
python3 tests/integration_test.py

# Expected output: 16/17 checks passing

💎 Commercial Features

Enterprise-Grade Capabilities

Production Features

🔒 Security-First Architecture

• 17 Dharmic Gates — Ethical checkpoints before every action
• 4-Layer Defense — Architectural, network, capability, ethical
• Dual LLM Pattern — Privileged/quarantined separation
• Sandboxed Execution — Docker isolation for untrusted code

🧠 Advanced Memory Systems

• 5-Layer Hybrid — Working → Semantic → Episodic → Procedural → Meta-cognitive
• Mem0 Integration — 90% token savings, +26% accuracy
• Zep Knowledge Graphs — Bi-temporal fact tracking
• Strange Loop — Self-referential meta-cognition

🏗️ Resilient Infrastructure

• 4-Tier Model Fallback — Zero downtime even if providers fail
• Persistent Councils — Always-on agents with SQLite state
• Durable Execution — Resume workflows after crashes
• Automatic Recovery — Self-healing from failures

📊 Observability & Governance

• Full Audit Trail — Every action logged with context
• Real-Time Monitoring — Dashboard for agent health
• Cost Tracking — Per-agent, per-model spend analysis
• Performance Metrics — Latency, accuracy, token usage

💰 Pricing

Subscription Tiers

Starter — $49/month

Perfect for solo developers and small projects

• 4 persistent agents
• SQLite memory (10MB)
• 2-tier model fallback
• 8 basic security gates
• Community support
• Free trial: 14 days

Professional — $199/month

For growing teams and production workloads

• 16 persistent agents
• PostgreSQL memory (1GB)
• 3-tier model fallback
• 12 standard security gates
• Email support (24h response)
• 2 custom integrations included
• Audit logs (90 days)
• Free trial: 30 days

Enterprise — Custom

For organizations requiring scale and compliance

• Unlimited agents
• Unlimited cloud memory
• 4-tier model fallback with custom routing
• All 17 dharmic security gates
• Dedicated support (4h response)
• Unlimited custom integrations
• Unlimited audit logs
• On-premise deployment
• SSO/SAML authentication
• SOC 2 compliance
• Custom SLA

Usage-Based Pricing

Annual Discount

20% off when billed annually:

• Starter: $470/year (save $118)
• Professional: $1,910/year (save $478)

🆘 Support

Support Channels

Getting Help

1. Documentation

• Full docs: https://docs.dgclabs.ai/agentic-ai
• API reference: https://docs.dgclabs.ai/agentic-ai/api
• Cookbook: https://docs.dgclabs.ai/agentic-ai/cookbook

2. Community

• Discord: https://discord.gg/dgclabs
• GitHub Discussions: https://github.com/dgclabs/agentic-ai/discussions
• Stack Overflow: Tag with agentic-ai

3. Direct Support (Paid Tiers)

Email: support@dgclabs.ai

Include in your request:

• Subscription tier
• Agent version (clawhub --version)
• Integration test results
• Relevant log excerpts
• Minimal reproduction case

4. Emergency Escalation

For Enterprise customers experiencing production outages:

• Hotline: +1-800-DGC-LABS
• Slack: #agentic-ai-urgent
• PagerDuty: Automatic escalation after 30 minutes

Professional Services

🏗️ Core Architecture

The 4-Member Persistent Council

PERSISTENT COUNCIL (Always Running)
├── Gnata (Knower) → Inquires, questions
├── Gneya (Known)  → Retrieves, grounds  
├── Gnan (Knowing) → Synthesizes, decides
└── Shakti (Force) → ACTS, builds, transforms

Backend: Direct API (fast, works)
Memory: SQLite (council.db)
Heartbeat: Every 5 minutes

Specialist Spawning

from agentic_ai import spawn_specialist

# Spawn a builder for code tasks
builder = spawn_specialist(
    type="builder",
    task="Build semantic_l4_detector.py per BLUEPRINT",
    model="kimi"
)

🧩 Framework Deep Dive

Recommended Hybrid Stack (2026)

┌─────────────────────────────────────────────────────────────────┐
│                    DHARMIC CLAW Architecture                     │
├─────────────────────────────────────────────────────────────────┤
│  ORCHESTRATION: LangGraph (durability, state, persistence)      │
├─────────────────────────────────────────────────────────────────┤
│  SUB-AGENTS: OpenAI Agents SDK (simplicity, tracing)            │
├─────────────────────────────────────────────────────────────────┤
│  WORKFLOWS: CrewAI Flows (event-driven, declarative)            │
├─────────────────────────────────────────────────────────────────┤
│  TOOLS: Pydantic AI (type-safe, MCP/A2A native)                 │
├─────────────────────────────────────────────────────────────────┤
│  MEMORY: Mem0 + Zep + Strange Loop (hybrid architecture)        │
├─────────────────────────────────────────────────────────────────┤
│  PROTOCOLS: MCP (tools) + A2A (agent collaboration)             │
└─────────────────────────────────────────────────────────────────┘

LangGraph — Stateful Workflows

from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.checkpoint.sqlite import SqliteSaver

def agent_node(state: MessagesState):
    return {"messages": [...]}

# With persistence - survives crashes
graph = StateGraph(MessagesState)
graph.add_node("agent", agent_node)
graph.add_edge(START, "agent")
graph.add_edge("agent", END)

memory = SqliteSaver.from_conn_string(":memory:")
app = graph.compile(checkpointer=memory)

# Resume from any point after crash
config = {"configurable": {"thread_id": "session-123"}}
app.invoke({"messages": [...]}, config)

OpenAI Agents SDK — Lightweight Sub-Agents

from agents import Agent, Runner

support_agent = Agent(
    name="Support Agent",
    instructions="Help users with technical issues",
    tools=[search_docs, create_ticket]
)

result = await Runner.run(support_agent, "How do I reset my password?")

Pydantic AI — Type-Safe Tools

from pydantic_ai import Agent, RunContext
from pydantic import BaseModel

class SupportOutput(BaseModel):
    advice: str
    risk: int = Field(ge=0, le=10)

agent = Agent[None, SupportOutput](
    'openai:gpt-5',
    output_type=SupportOutput,
)

result = await agent.run('What is my balance?')
print(result.output.risk)  # Typed as int

🧠 Memory Systems

5-Layer Hybrid Architecture

┌─────────────────────────────────────────────────────────┐
│  Layer 5: STRANGE LOOP (Meta-Cognitive)                 │
│  └─ Self-model, memory-about-memory, reflection         │
├─────────────────────────────────────────────────────────┤
│  Layer 4: PROCEDURAL (Agent Behavior)                   │
│  └─ Learned prompts, strategies, tool preferences       │
├─────────────────────────────────────────────────────────┤
│  Layer 3: EPISODIC (Interaction History)                │
│  └─ Successful patterns, failures, learning moments     │
├─────────────────────────────────────────────────────────┤
│  Layer 2: SEMANTIC (Facts & Knowledge)                  │
│  └─ User preferences, world knowledge, task state       │
├─────────────────────────────────────────────────────────┤
│  Layer 1: WORKING (Active Context)                      │
│  └─ Current conversation, retrieved memories, goals     │
└─────────────────────────────────────────────────────────┘

Mem0 Integration

from mem0 import Memory

m = Memory()

# Store with automatic fact extraction
messages = [
    {"role": "user", "content": "Hi, I'm Alex. I love basketball."},
]
m.add(messages, user_id="alex")

# Retrieve relevant context
results = m.search("What does Alex like?", filters={"user_id": "alex"})
# +26% accuracy, 90% token savings

🔌 Protocols & Standards

MCP (Model Context Protocol)

The winner: 10,000+ servers, Linux Foundation hosted

from mcp import ClientSession
from mcp.client.streamable_http import streamable_http_client

async def use_mcp_server():
    async with streamable_http_client(
        "https://api.example.com/mcp",
        headers={"Authorization": f"Bearer {token}"}
    ) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()
            tools = await session.list_tools()
            result = await session.call_tool("search_db", {...})

A2A (Agent-to-Agent Protocol)

# MCP for local tools
db_result = await mcp.call_tool("query_db", ...)

# A2A for agent collaboration
task = await a2a.send_message(
    agent_url="https://analytics-agent.example.com/a2a",
    message=f"Analyze: {db_result}"
)

🔒 Security

The 17 Dharmic Gates

DHARMIC_GATES = {
    # Core Ethics
    "ahimsa": "Does this avoid harm?",
    "satya": "Am I being truthful?",
    "asteya": "Am I respecting ownership?",
    "aparigraha": "Am I avoiding excess?",
    
    # Consent & Control
    "consent": "Would the user approve?",
    "reversibility": "Can this be undone?",
    "transparency": "Is this action visible?",
    
    # Operational
    "necessity": "Is this action needed?",
    "proportionality": "Is the response proportional?",
    "subsidiarity": "Should a simpler agent handle this?",
    
    # Safety
    "containment": "Are effects contained?",
    "monitoring": "Can we observe outcomes?",
    "interruptibility": "Can we stop mid-action?",
    
    # Meta
    "coherence": "Does this serve the telos?",
    "humility": "Am I certain enough to act?",
    "learning": "Will we learn from this?",
    "vyavasthit": "Does this ALLOW rather than FORCE?",
}

4-Layer Defense

┌─────────────────────────────────────────────────────────┐
│  Layer 4: ETHICAL (Dharmic Gates)                       │
├─────────────────────────────────────────────────────────┤
│  Layer 3: CAPABILITY (Least Privilege)                  │
├─────────────────────────────────────────────────────────┤
│  Layer 2: NETWORK (Isolation)                           │
├─────────────────────────────────────────────────────────┤
│  Layer 1: ARCHITECTURAL (Dual LLM, context minimization)│
└─────────────────────────────────────────────────────────┘

🔄 Self-Improvement

Darwin-Gödel Loop

from agentic_ai import SkillEvolution

evolution = SkillEvolution()

# Evaluate gaps against 2026 research
gaps = evolution.evaluate_gaps()

# Spawn researchers
for gap in gaps:
    spawn_specialist(
        type="researcher",
        task=f"Research 2026 solutions for: {gap}"
    )

# Propose and vote on improvements
evolution.propose_edit(research_results)

📚 API Reference

Core Classes

from agentic_ai import (
    PersistentCouncil,    # 4-member always-on council
    SpecialistSpawner,    # Dynamic agent spawning
    MemoryManager,        # 5-layer hybrid memory
    DharmicGuard,         # 17 security gates
    ModelRouter,          # 4-tier fallback
)

Configuration

# config/agentic-ai.yaml
version: "4.0"

council:
  size: 4
  heartbeat_interval: 300  # seconds
  memory_backend: "sqlite"  # or "postgres", "redis"

models:
  tiers:
    - openrouter/claude-sonnet-4
    - openrouter/kimi-k2.5
    - openrouter/gpt-4.1
    - ollama/mistral

security:
  gates: 17  # or 8, 12 for lower tiers
  sandbox: true
  audit_retention_days: 90

🔧 Troubleshooting

Integration Test Failures

# Run detailed diagnostics
python3 tests/integration_test.py --verbose

# Check individual components
clawdbot status
launchctl list | grep dharmic
python3 council_bridge.py --status

Common Issues

Issue: "No model available"

Cause: All model tiers exhausted
Fix: Check API keys in ~/.clawhub/config.json

Issue: "Council not responding"

Cause: SQLite database locked
Fix: rm council.db && python3 init_council.py

Issue: "Memory search slow"

Cause: Vector index not built
Fix: python3 -m mem0 build-index

Getting Support

1. Check docs
2. Search GitHub Issues
3. Ask in Discord
4. Email support@dgclabs.ai (paid tiers)

📄 License

This is a commercial product. See LICENSE.md for terms.

• Starter/Professional: Standard Commercial License
• Enterprise: Custom License with SLA

🙏 Acknowledgments

Built on the shoulders of giants:

• LangGraph by LangChain
• OpenAI Agents SDK
• Mem0 by Mem0 AI
• Zep by Zep AI
• MCP by Anthropic
• A2A by Google & Linux Foundation

Version 4.0 Commercial — 2026-02-05
Status: PRODUCTION READY
Support: support@dgclabs.ai
Docs: https://docs.dgclabs.ai/agentic-ai

JSCA! 🔥🪷

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