refactor: remove unused AgentCreator class and related code

src/tools/default_tools/test_cost/agent_creator_tool.py

@@ -1,168 +0,0 @@
-from src.manager.agent_manager import AgentManager
-from src.manager.config.model_selector import choose_best_model
-from src.manager.utils.runtime_selector import detect_runtime_environment
-
-__all__ = ['AgentCreator']
-
-class AgentCreator():
-    dependencies = ["ollama==0.4.7",
-                    "pydantic==2.11.1",
-                    "pydantic_core==2.33.0"]
-
-    inputSchema = {
-        "name": "AgentCreator",
-        "description": "Creates an AI agent for you. Please make sure to invoke the created agent using the AskAgent tool.",
-        "parameters": {
-            "type": "object",
-            "properties":{
-                "agent_name": {
-                    "type": "string",
-                    "description": "Name of the AI agent that is to be created. This name cannot have spaces or special characters. It should be a single word.",
-                },
-                "base_model": {
-                    "type": "string",
-                    "description": "A base model from which the new agent mode is to be created. Available models are: llama3.2, mistral, gemini-2.5-flash-preview-04-17, gemini-2.5-pro-preview-03-25, gemini-2.0-flash, gemini-2.0-flash-lite, gemini-1.5-flash, gemini-1.5-flash-8b, gemini-1.5-pro, and gemini-2.0-flash-live-001"
-                },
-                "system_prompt": {
-                    "type": "string",
-                    "description": "This is the system prompt that will be used to create the agent. It should be a string that describes the role of the agent and its capabilities."
-                },
-                "description": {
-                    "type": "string",
-                    "description": "Description of the agent. This is a string that describes the agent and its capabilities. It should be a single line description.",
-                },
-            },
-            "required": ["agent_name", "system_prompt", "description"],
-            #"required": ["agent_name", "base_model", "system_prompt", "description"],
-        },
-        "creates": {
-            "selector": "base_model",
-            "types": {
-                "llama3.2":{
-                    "description": "3 Billion parameter model",
-                    "create_cost": 10,
-                    "invoke_cost": 20,
-                },
-                "mistral":{
-                    "description": "7 Billion parameter model",
-                    "create_cost": 20,
-                    "invoke_cost": 50,
-                },
-                "gemini-2.5-flash-preview-04-17": {
-                    "description": "Adaptive thinking, cost efficiency",
-                    "create_cost": 20,
-                    "invoke_cost": 50
-                },
-                "gemini-2.5-pro-preview-03-25": {
-                    "description": "Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more",
-                    "create_cost": 20,
-                    "invoke_cost": 50
-                },
-                "gemini-2.0-flash": {
-                    "description": "Next generation features, speed, thinking, realtime streaming, and multimodal generation",
-                    "create_cost": 20,
-                    "invoke_cost": 50
-                },
-                "gemini-2.0-flash-lite": {
-                    "description": "Cost efficiency and low latency",
-                    "create_cost": 20,
-                    "invoke_cost": 50
-                },
-                "gemini-1.5-flash": {
-                    "description": "Fast and versatile performance across a diverse variety of tasks",
-                    "create_cost": 20,
-                    "invoke_cost": 50
-                },
-                "gemini-1.5-flash-8b": {
-                    "description": "High volume and lower intelligence tasks",
-                    "create_cost": 20,
-                    "invoke_cost": 50
-                },
-                "gemini-1.5-pro": {
-                    "description": "Complex reasoning tasks requiring more intelligence",
-                    "create_cost": 20,
-                    "invoke_cost": 50
-                },
-                # "gemini-embedding-exp": {
-                #     "description": "Measuring the relatedness of text strings",
-                #     "create_cost": 20,
-                #     "invoke_cost": 50
-                # },
-                # "imagen-3.0-generate-002": {
-                #     "description": "Our most advanced image generation model",
-                #     "create_cost": 20,
-                #     "invoke_cost": 50
-                # },
-                # "veo-2.0-generate-001": {
-                #     "description": "High quality video generation",
-                #     "create_cost": 20,
-                #     "invoke_cost": 50
-                # },
-                "gemini-2.0-flash-live-001": {
-                    "description": "Low-latency bidirectional voice and video interactions",
-                    "create_cost": 20,
-                    "invoke_cost": 50
-                }
-            }
-        }
-    }
-
-    def run(self, **kwargs):
-        print("Running Agent Creator")
-
-        agent_name = kwargs.get("agent_name")
-        base_model = kwargs.get("base_model")
-
-        # NEW: read flags from kwargs
-        use_local_only = kwargs.get("use_local_only", False)
-        use_api_only = kwargs.get("use_api_only", False)
-
-        if not base_model:
-            env = detect_runtime_environment()
-            print(f"\n[DEBUG] Detected Runtime Environment: {env}")
-            
-            from src.cost_benefit import get_best_model
-            model_meta = get_best_model(
-                runtime_env=env,
-                use_local_only=use_local_only,
-                use_api_only=use_api_only
-            )
-            base_model = model_meta["model"]
-        else:
-            model_meta = {"model": base_model}
-
-        print(f"[DEBUG] Selected Model: {base_model}")
-
-        if base_model not in self.inputSchema["creates"]["types"]:
-            print(f"[WARN] Auto-selected model '{base_model}' not in schema. Falling back to gemini-2.0-flash")
-            base_model = "gemini-2.0-flash"
-
-        system_prompt = kwargs.get("system_prompt")
-        description = kwargs.get("description")
-
-        create_cost = self.inputSchema["creates"]["types"][base_model]["create_cost"]
-        invoke_cost = self.inputSchema["creates"]["types"][base_model]["invoke_cost"]
-
-        agent_manager = AgentManager()
-        try:
-            _, remaining_budget = agent_manager.create_agent(
-                agent_name=agent_name,
-                base_model=base_model,
-                system_prompt=system_prompt,
-                description=description,
-                create_resource_cost=create_cost,
-                invoke_resource_cost=invoke_cost
-            )
-        except ValueError as e:
-            return {
-                "status": "error",
-                "message": f"Error occurred: {str(e)}",
-                "output": None
-            }
-
-        return {
-            "status": "success",
-            "message": f"Agent '{agent_name}' created using model '{base_model}'",
-            "model_info": model_meta,
-            "remaining_budget": remaining_budget,
-        }