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Introduction

Welcome to Bizy - an enterprise-grade solution for orchestrating AI frameworks with business logic.

What is Bizy?​

Bizy provides a unified abstraction layer that enables you to:

  • 🔗 Integrate Multiple AI Frameworks - Work seamlessly with LangChain, Temporal, MCP, Semantic Kernel, FastMCP, and Zep AI
  • 📋 Define Business Rules Once - Use YAML-based rules that work across all frameworks
  • 🚀 Build Complex Workflows - Coordinate multi-step processes with error handling and monitoring
  • 📊 Enterprise-Grade Features - Production-ready with security, scalability, and observability

Why Bizy?​

Modern AI applications often require multiple frameworks working together:

  • LangChain for LLM interactions
  • Temporal for workflow orchestration
  • MCP for tool integration
  • Semantic Kernel for AI agent coordination
  • FastMCP for high-performance tools
  • Zep for memory management

Without proper orchestration, integrating these frameworks leads to:

  • Complex, tightly-coupled code
  • Duplicated business logic
  • Difficult maintenance and testing
  • Limited reusability

Bizy solves these challenges by providing a framework-agnostic layer for business logic orchestration.

Quick Example​

# Define a business rule
rule: customer_escalation
conditions:
  - customer.tier == "premium"
  - sentiment.score < 0.5
actions:
  - framework: langchain
    action: analyze_detailed
  - framework: temporal
    action: create_priority_ticket
  - framework: mcp
    action: notify_manager
# Execute across frameworks
result = await orchestrator.evaluate_and_execute(
    rule_set="customer_service",
    context={
        "customer": customer_data,
        "sentiment": sentiment_result
    }
)

Getting Started​

Ready to orchestrate your AI frameworks?

Get Started →

Key Features​

🎯 Business Rule Engine​

  • YAML-based rule definitions
  • Complex condition evaluation
  • Priority-based execution
  • Conflict resolution

🔄 Framework Adapters​

  • Consistent interface across frameworks
  • Connection pooling and health checks
  • Automatic retry and failover
  • Framework-specific optimizations

📡 Event-Driven Architecture​

  • Real-time cross-framework communication
  • Event persistence and replay
  • Distributed event processing
  • Audit trail generation

🛡️ Enterprise Security​

  • Role-based access control
  • API key management
  • Audit logging
  • Data encryption

📈 Monitoring & Observability​

  • Prometheus metrics
  • Distributed tracing
  • Health dashboards
  • Performance analytics

Architecture Overview​

graph TD
    A[Business Logic Layer] --> B[Meta-Orchestrator]
    B --> C[LangChain Adapter]
    B --> D[Temporal Adapter]
    B --> E[MCP Adapter]
    B --> F[Other Adapters]
    
    G[Event Bus] --> B
    H[Rule Engine] --> B
    
    style A fill:#f9f,stroke:#333,stroke-width:4px
    style B fill:#bbf,stroke:#333,stroke-width:2px

Community & Support​

Join our growing community:

  • 💬 Discord - Get help and share experiences
  • 🐛 GitHub Issues - Report bugs and request features
  • 📖 Blog - Latest updates and tutorials
  • 🤝 Contributing - Help improve the project

Next Steps​