orka.cli.core module
CLI Core Functionality
This module contains the core CLI functionality including the programmatic entry point for running OrKa workflows.
- async orka.cli.core.run_cli_entrypoint(config_path: str, input_text: str, log_to_file: bool = False, verbose: bool = False) dict[str, Any] | list[Event] | str[source]
🚀 Primary programmatic entry point - run OrKa workflows from any application.
What makes this special: - Universal Integration: Call OrKa from any Python application seamlessly - Flexible Output: Returns structured data perfect for further processing - Production Ready: Handles errors gracefully with comprehensive logging - Development Friendly: Optional file logging for debugging workflows
Integration Patterns:
1. Simple Q&A Integration:
result = await run_cli_entrypoint( "configs/qa_workflow.yml", "What is machine learning?", log_to_file=False
) # Returns: {“answer_agent”: “Machine learning is…”} ```
2. Complex Workflow Integration: ```python result = await run_cli_entrypoint(
- “configs/content_moderation.yml”,
user_generated_content, log_to_file=True # Debug complex workflows
) # Returns: {“safety_check”: True, “sentiment”: “positive”, “topics”: [“tech”]}
3. Batch Processing Integration:
results = [] for item in dataset: result = await run_cli_entrypoint( "configs/classifier.yml", item["text"], log_to_file=False ) results.append(result)
Return Value Intelligence: - Dict: Agent outputs mapped by agent ID (most common) - List: Complete event trace for debugging complex workflows - String: Simple text output for basic workflows
Perfect for: - Web applications needing AI capabilities - Data processing pipelines with AI components - Microservices requiring intelligent decision making - Research applications with custom AI workflows