# freephdlabor/agents/my_custom_agent.py
from .base_research_agent import BaseResearchAgent
from typing import List
from smolagents import Tool

class MyCustomAgent(BaseResearchAgent):
    """Custom agent for specialized research tasks."""
    
    def __init__(self, model, workspace_dir: str, workspace_interpreter=None, **kwargs):
        # Define your custom tools
        tools = [
            MyCustomTool(),
            # Add other tools as needed
        ]
        
        # Create system prompt for your domain
        system_prompt = """
        You are MyCustomAgent, specialized in [your domain].
        
        Your capabilities include:
        - [Capability 1]
        - [Capability 2] 
        - [Capability 3]
        
        Use your tools to accomplish research tasks in [your domain].
        Always coordinate with other agents and maintain research continuity.
        """
        
        super().__init__(
            model=model,
            agent_name="my_custom_agent",
            workspace_dir=workspace_dir,
            workspace_interpreter=workspace_interpreter,
            tools=tools,
            system_prompt=system_prompt,
            **kwargs
        )

# freephdlabor/toolkits/my_custom_tool.py
from smolagents import Tool
from typing import Any, Dict

class MyCustomTool(Tool):
    """Custom tool for specialized research tasks."""
    
    def __init__(self):
        super().__init__(
            name="my_custom_tool",
            description="Description of what this tool does"
        )
    
    def __call__(self, input_data: str, **kwargs) -> str:
        """Execute the tool with given input."""
        # Implement your tool logic here
        return "Tool execution result"

# my_custom_system.py
from freephdlabor.agents.manager_agent import ManagerAgent
from freephdlabor.agents.my_custom_agent import MyCustomAgent
from freephdlabor.agents.ideation_agent import IdeationAgent
from freephdlabor.agents.experimentation_agent import ExperimentationAgent
from freephdlabor.agents.writeup_agent import WriteupAgent
from freephdlabor.interpreters.workspace_executor import WorkspacePythonExecutor

def initialize_my_custom_system(model, workspace_dir, workspace_interpreter, essential_imports, **kwargs):
    """
    Initialize your custom multiagent system.
    
    This function gives you complete control over:
    - Which agents to include
    - How they're configured
    - How they interact with each other
    - Custom workflows and coordination
    """
    print("🔧 Initializing custom multiagent system...")
    
    # Create your custom agents
    custom_agent = MyCustomAgent(
        model=model,
        workspace_dir=workspace_dir,
        workspace_interpreter=workspace_interpreter,
        additional_authorized_imports=essential_imports
    )
    
    # Include standard agents if needed
    ideation_agent = IdeationAgent(
        model=model,
        workspace_dir=workspace_dir,
        workspace_interpreter=workspace_interpreter,
        additional_authorized_imports=essential_imports
    )
    
    experimentation_agent = ExperimentationAgent(
        model=model,
        workspace_dir=workspace_dir,
        workspace_interpreter=workspace_interpreter,
        additional_authorized_imports=essential_imports
    )
    
    writeup_agent = WriteupAgent(
        model=model,
        workspace_dir=workspace_dir,
        workspace_interpreter=workspace_interpreter,
        additional_authorized_imports=essential_imports
    )
    
    # Create your custom manager with your agent configuration
    manager = ManagerAgent(
        model=model,
        workspace_dir=workspace_dir,
        workspace_interpreter=workspace_interpreter,
        additional_authorized_imports=essential_imports,
        # Pass your custom agents
        custom_agents=[custom_agent],
        # Configure which standard agents to include
        include_ideation=True,
        include_experimentation=True,
        include_writeup=True,
        include_reviewer=False,  # Skip reviewer if not needed
        include_proofreading=False,  # Skip proofreading if not needed
        **kwargs
    )
    
    return manager

# launch_my_custom_system.py
import argparse
import os
import sys
from datetime import datetime

# Add the current directory to Python path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

from freephdlabor.utils import create_model
from my_custom_system import initialize_my_custom_system

def main():
    """Main entry point for your custom multiagent system."""
    parser = argparse.ArgumentParser(description="Custom Multiagent Research System")
    parser.add_argument("--model", type=str, default="gpt-5", help="LLM model to use")
    parser.add_argument("--workspace", type=str, default="my_research", help="Workspace directory")
    parser.add_argument("--task", type=str, help="Research task to execute")
    args = parser.parse_args()
    
    # Create model
    model = create_model(args.model)
    
    # Create workspace directory
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    workspace_dir = os.path.join("results", f"{timestamp}_{args.workspace}")
    os.makedirs(workspace_dir, exist_ok=True)
    
    # Essential imports for agents
    essential_imports = [
        "json", "os", "sys", "datetime", "typing", "pathlib", "shutil", 
        "functools", "copy", "pickle", "logging", "warnings", "gc",
        "argparse", "configparser", "yaml", "toml", "requests", "urllib",
        "datasets", "transformers", "huggingface_hub", "tokenizers",
        "wandb", "tensorboard", "tqdm", "requests", "urllib", "zipfile", "tarfile"
    ]
    
    # Create workspace executor
    workspace_executor = WorkspacePythonExecutor(
        workspace_dir=workspace_dir,
        additional_authorized_imports=essential_imports
    )
    
    # Initialize your custom system
    manager = initialize_my_custom_system(
        model=model,
        workspace_dir=workspace_dir,
        workspace_interpreter=workspace_executor,
        essential_imports=essential_imports
    )
    
    # Define your task
    task = args.task or "Execute your custom research workflow"
    
    print(f"🚀 Starting custom multiagent system...")
    print(f"📁 Workspace: {workspace_dir}")
    print(f"📝 Task: {task}")
    
    # Run your custom system
    result = manager.run(task)
    
    print(f"✅ Custom system completed!")
    print(f"📋 Result: {result}")

if __name__ == "__main__":
    main()

def initialize_computer_vision_system(model, workspace_dir, workspace_interpreter, essential_imports, **kwargs):
    """Initialize a computer vision research system."""
    
    # Create CV-specific agents
    cv_analysis_agent = ComputerVisionAnalysisAgent(
        model=model, workspace_dir=workspace_dir, workspace_interpreter=workspace_interpreter
    )
    
    cv_experiment_agent = ComputerVisionExperimentAgent(
        model=model, workspace_dir=workspace_dir, workspace_interpreter=workspace_interpreter
    )
    
    cv_visualization_agent = ComputerVisionVisualizationAgent(
        model=model, workspace_dir=workspace_dir, workspace_interpreter=workspace_interpreter
    )
    
    # Create custom manager for CV research
    manager = ComputerVisionManagerAgent(
        model=model,
        workspace_dir=workspace_dir,
        workspace_interpreter=workspace_interpreter,
        cv_agents=[cv_analysis_agent, cv_experiment_agent, cv_visualization_agent]
    )
    
    return manager

def initialize_minimal_system(model, workspace_dir, workspace_interpreter, essential_imports, **kwargs):
    """Initialize a minimal research system with only essential agents."""
    
    # Only include ideation and writeup agents
    ideation_agent = IdeationAgent(
        model=model, workspace_dir=workspace_dir, workspace_interpreter=workspace_interpreter
    )
    
    writeup_agent = WriteupAgent(
        model=model, workspace_dir=workspace_dir, workspace_interpreter=workspace_interpreter
    )
    
    # Create minimal manager
    manager = MinimalManagerAgent(
        model=model,
        workspace_dir=workspace_dir,
        workspace_interpreter=workspace_interpreter,
        agents=[ideation_agent, writeup_agent]
    )
    
    return manager