Returns system information including:
    - ComfyUI and PyTorch versions
    - RAM usage
    - Device info (CPU/GPU)

    Use this to verify ComfyUI is running and check resource usage.
    

    Returns:
    - queue_running: List of currently executing workflows
    - queue_pending: List of queued workflows waiting to run
    - running_count: Number of running jobs
    - pending_count: Number of pending jobs

    Use this to check if workflows are executing or queued.
    

    Args:
        limit: Maximum number of history entries (1-100, default: 10)

    Returns history entries with outputs and status for each job.
    Use this to see past generations and their results.
    

    Args:
        prompt_id: Optional specific prompt ID to cancel.
                  If not provided, cancels all running jobs.

    Use this to stop a long-running generation.
    

    Args:
        delete_ids: Optional list of prompt IDs to delete.
                   If not provided, clears entire queue.

    Use this to remove pending jobs from the queue.
    

    Args:
        filter: Optional string to match node names (case-insensitive)
        category: Optional category filter (exact match)

    Returns a sorted list of node class names.
    Use this to discover available nodes for workflow building.
    

    Args:
        node_name: Node class name (e.g., 'RemoteCheckpointLoader_fal')

    Returns node information including:
    - input: Required and optional inputs with types
    - output: Output types
    - category: Node category
    - description: What the node does

    Use this to understand how to configure a node in a workflow.
    

    Args:
        folder: Model folder name. Options:
            - checkpoints: Full model checkpoints
            - loras: LoRA fine-tuning files
            - vae: VAE decoders
            - embeddings: Text embeddings
            - controlnet: ControlNet models
            - upscale_models: Upscaling models
            - clip_vision: CLIP vision encoders

    Returns list of model filenames in the folder.
    

    Returns a list of valid folder names for list_models().
    

    Call this after installing new custom nodes to see them in list_nodes().
    

    Args:
        query: Search string (searches name, category, description)

    Returns matching nodes sorted by relevance.
    

    Returns list of workflow JSON files in the configured workflows directory.
    Use run_workflow() to execute a saved workflow.
    

    Args:
        workflow_name: Workflow filename (e.g., 'my-workflow.json')

    Returns the workflow dict that can be modified and executed.
    

    Args:
        workflow: Workflow dict to save (in API format)
        name: Filename (with or without .json extension).
              If not provided, generates a random funny name like 'cosmic-penguin'.
        format: Output format:
            - 'api': Raw API format for execution (flat dict)
            - 'ui': Litegraph format for ComfyUI editor (default)

    Returns path to saved file or error message.
    

    Returns an empty dict that you can populate with add_node().
    

    Args:
        words: Number of words in the name (2-4, default: 2)

    Returns a slug like 'cosmic-penguin' or 'mighty-purple-narwhal'.
    Use this when saving new workflows for creative naming.
    

    Args:
        workflow: Existing workflow dict
        node_id: Unique identifier for this node
        node_type: Node class name (use list_nodes() to find)
        inputs: Input values. For connections use ["source_node_id", output_index].

    Examples:
        # Simple value input
        add_node(wf, "1", "StringInput_fal", {"text": "a cat"})

        # Connection to another node
        add_node(wf, "2", "CLIPTextEncode", {
            "text": "prompt",
            "clip": ["1", 0]  # Connect to node "1" output 0
        })

    Returns the modified workflow dict.
    

    Args:
        workflow: Workflow dict to modify
        node_id: ID of node to remove

    Warning: This doesn't update connections from other nodes.
    

    Args:
        workflow: Workflow dict to modify
        node_id: Node ID to update
        input_name: Name of the input to update
        value: New value (use JSON string for lists/dicts)

    Returns the modified workflow dict.
    

    Args:
        template_name: One of:
            - 'empty': Empty workflow
            - 'fal-flux-dev': Flux Dev via fal.ai (higher quality)
            - 'fal-flux-schnell': Flux Schnell via fal.ai (faster)

    Returns a workflow dict that can be modified and executed.
    

    Returns list of template names for get_workflow_template().
    

    Args:
        workflow: Workflow dict to validate

    Returns validation result with any issues found.
    

    Args:
        workflow: Workflow in API format (flat dict with class_type/inputs)

    Returns:
        Workflow in UI format compatible with ComfyUI editor.
        This format includes node positions, visual links, and metadata
        required for the workflow to be displayed and edited in the UI.

    The conversion:
    - Assigns integer IDs to nodes
    - Places nodes in a grid layout
    - Creates visual links from input connections
    - Sets appropriate node sizes based on type
    

    Args:
        workflow_name: Workflow filename (e.g., 'flux-dev.json')
        inputs: Optional input overrides, e.g., {"6": {"text": "new prompt"}}
        output_node_id: Node ID to get output from (uses default if not set)

    Returns the generated image or error message.
    

    Args:
        workflow: Workflow dict in ComfyUI API format
        output_node_id: Node ID that outputs the final image

    Returns the generated image or error message.
    Use this for programmatically built workflows.
    

    This is a simplified interface for quick image generation.
    Requires COMFY_WORKFLOW_JSON_FILE, PROMPT_NODE_ID, and OUTPUT_NODE_ID
    to be configured.

    For more control, use run_workflow() or execute_workflow().

    Args:
        prompt: Text description of the image to generate
    

    Args:
        workflow: Workflow dict to execute

    Returns the prompt_id for tracking.
    Use get_history() or get_prompt_status() to check completion.
    

    Args:
        prompt_id: The prompt ID from submit_workflow()

    Returns status information including completion state.
    

    Args:
        prompt_id: The prompt ID from submit_workflow()
        output_node_id: Node ID that produced the image

    Returns the image if available, or error message.