Class OpenSpaceConfig

openspace/tool_layer.py:24–76

@dataclass
class OpenSpaceConfig:
    # LLM Configuration
    llm_model: str = "openrouter/anthropic/claude-sonnet-4.5"
    llm_enable_thinking: bool = False
    llm_timeout: float = 120.0
    llm_max_retries: int = 3
    llm_rate_limit_delay: float = 0.0
    llm_kwargs: Dict[str, Any] = field(default_factory=dict)
    
    # Separate models for specific tasks (None = use llm_model)
    tool_retrieval_model: Optional[str] = None  # Model for tool retrieval LLM filter
    visual_analysis_model: Optional[str] = None  # Model for visual analysis
    
    # Skill Engine Models — names map to class names (None = use llm_model)
    skill_registry_model: Optional[str] = None        # SkillRegistry: skill selection
    execution_analyzer_model: Optional[str] = None    # ExecutionAnalyzer: post-execution analysis
    skill_evolver_model: Optional[str] = None         # (future) SkillEvolver: skill evolution
    
    # Grounding Configuration
    grounding_config_path: Optional[str] = None
    grounding_max_iterations: int = 20
    grounding_system_prompt: Optional[str] = None
    
    # Backend Configuration
    backend_scope: Optional[List[str]] = None  # None = All backends ["shell", "gui", "mcp", "web", "system"]
    use_clawwork_productivity: bool = False  # If True, add ClawWork productivity tools (search_web, create_file, etc.) for fair comparison with ClawWork; requires livebench installed.
    
    # Workspace Configuration
    workspace_dir: Optional[str] = None
    
    # Recording Configuration
    enable_recording: bool = True
    recording_backends: Optional[List[str]] = None
    recording_log_dir: str = "./logs/recordings"
    enable_screenshot: bool = False
    enable_video: bool = False
    enable_conversation_log: bool = True  # Save LLM conversations to conversations.jsonl
    
    # Skill Evolution
    evolution_max_concurrent: int = 3        # Max parallel evolutions per trigger
    execution_analysis_timeout: float = 300.0  # Overall post-task analyzer/evolver bound
    
    # Logging Configuration
    log_level: str = "INFO"
    log_to_file: bool = False
    log_file_path: Optional[str] = None
    
    def __post_init__(self):
        """Validate configuration"""
        if not self.llm_model:
            raise ValueError("llm_model is required")
        
        logger.debug(f"OpenSpaceConfig initialized with model: {self.llm_model}")


class OpenSpace: