content: #!/usr/bin/env python3
  """Simple runner script for the Agent Architecture Comparison example.
  
  This provides a clean entry point for users who want to run the example
  without needing to understand the full pipeline implementation.
  """
  
  from pipelines import compare_agent_architectures
  
  
  def main() -> None:
      """Run the agent architecture comparison pipeline."""
      print("🚀 Starting Agent Architecture Comparison Pipeline...")
      print("This will demonstrate:")
      print("  • Training a scikit-learn intent classifier")
      print("  • Running 3 different agent architectures")
      print("  • LiteLLM integration (real LLMs if API keys detected)")
      print("  • Generating LangGraph workflow visualizations")
      print("  • Creating beautiful HTML comparison reports")
      print()
  
      # Check if real LLMs will be used
      from llm_utils import should_use_langfuse, should_use_real_llm
  
      if should_use_real_llm():
          print("✨ Real LLM APIs detected! Using LiteLLM for agent responses.")
          if should_use_langfuse():
              print(
                  "📊 Langfuse observability enabled! Tracking costs and performance."
              )
          else:
              print(
                  "💡 Add LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY for observability."
              )
      else:
          print(
              "📝 No LLM API keys found. Using mock responses (perfect for demos)."
          )
          print("💡 Set OPENAI_API_KEY to enable real LLM integration.")
      print()
  
      # Execute the pipeline
      compare_agent_architectures()
  
      print("✅ Pipeline completed successfully!")
      print()
      print("🎯 Check the ZenML Dashboard to see:")
      print("  • Customer service queries dataset")
      print("  • Trained intent classifier model")
      print("  • Architecture performance metrics")
      print("  • Interactive Mermaid diagrams for all three architectures")
      print("  • Beautiful HTML comparison report")
      if should_use_langfuse():
          print("  • Langfuse traces with cost and performance data")
      print()
      print("💡 Start the dashboard with: zenml login")
  
  
  if __name__ == "__main__":
      main()
  