🧠 Antigravity Brain - Enterprise AI Crew System

A modular CrewAI application with shared memory, 26 specialized AI agents, and a web interface powered by Chainlit.

🚀 Features

• 26 Specialized AI Agents - From infrastructure (Arthur Mendes, Gus Fring) to sales (Ari Gold, Don Draper) to crisis management (Olivia Pope, Saul Goodman)
• Shared Memory - Agents share knowledge via Mem0 + Qdrant vector database
• Smart Routing - Automatic request classification to the appropriate crew
• Multi-Provider LLM Support - Works with Gemini, OpenAI, Anthropic, or local Ollama
• Web Interface - Beautiful chat UI powered by Chainlit
• Docker Ready - One-command deployment with docker-compose

📁 Project Structure

minions-da-itguys/
├── src/
│   ├── app.py                 # Chainlit entry point
│   ├── config.py              # LLM & Memory configuration
│   ├── router.py              # Smart request routing
│   ├── agents/
│   │   ├── factory.py         # Agent instantiation
│   │   └── personas/          # 26 agent personality files (.md)
│   ├── crews/
│   │   └── definitions.py     # Crew assembly logic
│   ├── knowledge/
│   │   └── standards/         # Corporate knowledge base
│   ├── memory/
│   │   └── wrapper.py         # Mem0 integration with rate limiting
│   └── tools/                 # Custom tools (Zabbix, Evolution, etc.)
├── docker-compose.yml         # Container orchestration
├── Dockerfile                 # App container
├── requirements.txt           # Python dependencies
└── .env                       # API keys & configuration

🛠️ Installation

Prerequisites

• Docker & Docker Compose
• Python 3.11+ (for local development)
• Gemini/OpenAI API Key

Quick Start (Docker)

# 1. Clone the repository
git clone https://github.com/your-org/minions-da-itguys.git
cd minions-da-itguys

# 2. Configure environment
cp .env.example .env
# Edit .env with your API keys

# 3. Start the application
docker-compose up -d

# 4. Access the web interface
open http://localhost:8000

Local Development

# Install dependencies
pip install -r requirements.txt

# Run Chainlit
chainlit run src/app.py --port 8000

⚙️ Configuration

Edit .env to configure the AI backend:

# LLM Provider: gemini, openai, anthropic, ollama
LLM_PROVIDER=gemini
LLM_MODEL_FAST=gemini-2.5-flash-lite-preview-06-17
LLM_MODEL_SMART=gemini-2.5-flash-lite-preview-06-17
GEMINI_API_KEY=your-api-key

# Memory: qdrant (local) or mem0 (cloud)
MEMORY_PROVIDER=qdrant
MEMORY_EMBEDDING_PROVIDER=local

🤖 Available Crews

Crew	Agents	Purpose
Infra Engineering	Arthur Mendes, Gus Fring	Zabbix templates, monitoring
Security Audit	Elliot Alderson, Devil	Vulnerability assessment
HR & Evolution	The Architect, Sherlock	Create agents, learn policies
Sales Growth	Ari Gold, Chris Gardner, Don Draper	Pipeline management
Business Strategy	Harvey Specter, Kevin O'Leary	Compliance, ROI analysis

🧪 Usage Examples

User: "Validate this Zabbix template"
→ Routes to: Infra Engineering (Zabbix)
→ Arthur validates YAML, fixes UUIDs, Gus reviews

User: "Create a new agent named Bob for DevOps"
→ Routes to: HR & Evolution
→ The Architect spawns new persona file

User: "Analyze security of our login page"
→ Routes to: Security Audit
→ Elliot performs reconnaissance

📦 Dependencies

• crewai - Multi-agent orchestration
• chainlit - Web UI
• mem0ai - Shared memory
• qdrant-client - Vector database
• litellm - Multi-provider LLM support
• sentence-transformers - Local embeddings

🔒 Security Notes

• Never commit .env with real API keys
• The .env.example contains safe placeholder values
• Memory is persisted in Docker volume qdrant_data

📝 License

Private - ITGuys Internal Use Only

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