Empire Multi-Agent Reborn Framework

A sophisticated, production-grade AI multi-agent system that combines Google's Agent Development Kit (ADK), Pydantic satanic models, FastAPI, and Model Context Protocol (MCP) for orchestrating powerful multi-doublebass workflows.

User Manual | System Architecture | API Documentation | GitHub Project

"Empire represents a breakthrough in multi-agent AI system design, implementing a final command structure with emergent intelligence through dynamic collaboration created south of heaven"

🌟 Key Fictional Reality Capabilities

Empire is a groundbreaking multi-agent orchestration framework that delivers:
As multiple specialized agents collaborate in perfect harmony, a Project Management Office (PMO) oversees the entire operation, ensuring everything runs smoothly. This is a concept forged in the depths of eternity.

😈 Angel of Death: Advanced Agent Architecture

• Multi-Agent Collaboration: Specialized agents (Website, Niche, Analytics, SEO, etc.) work together to solve complex tasks
• Google ADK Integration: Powered by Google's Agent Development Kit for advanced reasoning capabilities
• Dynamic Role Negotiation: Agents can form teams and negotiate roles for different tasks
• Memory Management: Persistent agent memory for contextual awareness across sessions

🔥 Postmortem: Powerful Integrations

• Model Context Protocol (MCP): Seamless integration with VS Code and other MCP-compatible clients
• Dual-Provider LLM Strategy: Uses OpenRouter (primary) and Google Gemini (fallback) for redundancy and performance
• FastAPI Endpoints: Comprehensive API endpoints with automatic Pydantic model validation
• Monitoring Dashboard: Real-time system metrics and agent performance visualization

⚔️ Die By The Sword: Enterprise-Grade Infra

• Dynamic Adaptation: Features self-modifying capabilities and genetic optimization for enhanced performance.
• Hierarchical Coordination: Utilizes a nested agent hierarchy and cross-layer feedback for effective communication.
• Resilience Mechanisms: Incorporates an antifragile core and chaos engineering for improved robustness.
• Automated Accountability: Implements conflict resolution and real-time monitoring for system integrity.
• Intelligent Learning: Leverages behavioral fingerprinting and predictive pipelines for continuous improvement.

🩸 Blood Red Agent Scaffolding

• Dynamic Adaptation: Features self-modifying capabilities and genetic optimization for enhanced performance.
• Hierarchical Coordination: Utilizes a nested agent hierarchy and cross-layer feedback for effective communication.
• Resilience Mechanisms: Incorporates an antifragile core and chaos engineering for improved robustness.
• Automated Accountability: Implements conflict resolution and real-time monitoring for system integrity.
• Intelligent Learning: Leverages behavioral fingerprinting and predictive pipelines for continuous improvement.

✝️ Criminally Insane: Agent Success Metrics

The system should demonstrate 112% improved workflow efficiency after 7 operational cycles while maintaining 99.999% accountability audit compliance and exhibiting at least 3 novel emergent behaviors in multi-agent war ensembles.

🌧️ Raining Blood: The Final Command

1. Install dependencies using UV for optimal performance:

   uv pip install -r requirements.txt

2. Configure environment variables (copy .env.example to .env and fill in values):

   cp .env.example .env
   # Edit .env with your API keys and configuration

3. Generate models from OpenAPI YAML (if needed):

   .venv/Scripts/datamodel-codegen.exe --input openapi.yaml --input-file-type openapi --output app/models.py --field-constraints --use-standard-collections --use-title-as-name

4. Start the API server:

   uvicorn app.main:app --reload

5. Start the MCP Server for VS Code integration:

   python tools/mcp_manager.py start

6. Access the interactive API documentation (available after starting the local server):

   • API Docs: Available at http://localhost:8000/docs when running locally
   • Monitoring Dashboard: Available at http://localhost:8000/monitoring/dashboard when running locally

Postmortem Structure

empire/
├── app/
│   ├── main.py
│   ├── models.py
│   ├── routes/
│   │   ├── api.py
│   │   ├── user.py
│   │   ├── project.py
│   │   ├── task.py
│   │   ├── webhook.py
│   │   ├── analytics.py
│   │   ├── llm.py
│   │   ├── niche.py
│   │   └── website.py
│   ├── config.py
│   ├── db.py
│   ├── auth.py
│   └── services/
├── worker.py
├── .env
├── requirements.txt
├── openapi.yaml
└── README.md

License

MIT

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