SCIRM is an intelligent agent swarm system that proactively predicts, monitors, and mitigates supply chain risks through real-time data analysis and AI-driven recommendations. The platform integrates Context-Augmented Generation (CAG) for organizational insights and Retrieval-Augmented Generation (RAG) for live data processing.
- Multi-Agent Swarm Architecture: Coordinated AI agents for specialized risk management tasks
- Real-Time Risk Prediction: Sub-500ms response times for critical supply chain alerts
- Explainable AI: All recommendations include reasoning trails and confidence scores
- Industry Focus: Initially targeting pharmaceutical & healthcare with extensibility
- Compliance Ready: SOC2, GDPR, HIPAA alignment with audit trails
- Coordinator Agent: Orchestrates agent collaboration and monitors task state
- Planner Agent (CAG): Maintains organizational context and priorities
- Researcher Agent (RAG): Fetches and ranks real-time data from multiple sources
- Executor Agent: Generates actionable risk mitigation recommendations
- Quality Reviewer Agent: Validates outputs and assigns confidence scores
- Backend/AI: Python, FastAPI, LangChain, LangGraph, GPT-4/Claude
- Vector DB: Pinecone/Weaviate for embeddings, Elasticsearch for search
- Frontend: React/Next.js, Tailwind CSS, Chart.js/D3.js
- Infrastructure: Docker, Kubernetes, AWS/Azure/GCP
- Monitoring: Prometheus, Grafana, structured logging
SCIRM_Monorepo/
├── apps/ # Frontend and API gateway
│ ├── frontend/ # React dashboard
│ └── api-gateway/ # FastAPI gateway
├── services/ # Agent microservices
│ ├── coordinator/ # Meta-agent orchestration
│ ├── planner/ # CAG context agent
│ ├── researcher/ # RAG data agent
│ ├── executor/ # Action recommendation agent
│ └── reviewer/ # Quality validation agent
├── libs/ # Shared libraries
│ ├── common/ # Utilities and types
│ ├── rag/ # RAG implementation
│ └── cag/ # CAG implementation
├── data/ # Fixtures and seeds
├── infra/ # Infrastructure as Code
├── ops/ # CI/CD and monitoring
├── docs/ # MkDocs documentation
└── tests/ # Integration tests
- Python 3.11+
- Node.js 18+
- Docker & Docker Compose
- Make
# Clone repository
git clone https://github.com/DevilsDev/SCIRM_Monorepo.git
cd SCIRM_Monorepo
# Install dependencies
make install
# Start development environment
make dev
# Run tests
make test
# Build documentation
make docs# Copy environment template
cp .env.example .env
# Configure required variables
# - OpenAI/Anthropic API keys
# - Vector database credentials
# - External data source APIs- ERP systems (SAP, Oracle)
- Warehouse Management Systems (WMS)
- Transportation Management Systems (TMS)
- IoT sensor networks
- Weather and climate data
- Logistics and shipping APIs
- Regulatory and compliance feeds
- Market and economic indicators
- Supplier Risk Assessment: Financial stability, geopolitical factors
- Logistics Disruption Prediction: Weather, traffic, port congestion
- Regulatory Compliance Monitoring: FDA, EMA, customs requirements
- Demand Forecasting: Market trends, seasonal patterns
- Quality Control: Product recalls, contamination risks
- Performance Metrics: Response times, throughput, error rates
- Business Metrics: Risk prediction accuracy, mitigation effectiveness
- System Health: Service availability, resource utilization
- Audit Trails: Decision logs, data lineage, compliance reports
- Authentication: OAuth2/JWT with RBAC
- Data Protection: TLS encryption, secrets management
- Compliance: SOC2, GDPR, HIPAA frameworks
- Audit Logging: Immutable decision trails
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit changes (
git commit -m 'Add amazing feature') - Push to branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Documentation: https://devilsdev.github.io/SCIRM_Monorepo/
- Issues: GitHub Issues
- Discussions: GitHub Discussions