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from src.agent_manager import AgentManager
__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-2.0-flash"
},
"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", "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-2.0-flash": {
"description": "40 Billion parameter model",
"create_cost": 30,
"invoke_cost": 60,
}
}
}
}
def run(self, **kwargs):
print("Running Agent Creator")
agent_name = kwargs.get("agent_name")
base_model = kwargs.get("base_model")
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:
agent_manager.create_agent(
agent_name=agent_name,
base_model=base_model,
system_prompt=system_prompt,
description=description,
create_cost=create_cost,
invoke_cost=invoke_cost
)
except ValueError as e:
return {
"status": "error",
"message": f"Error occurred: {str(e)}",
"output": None
}
return {
"status": "success",
"message": "Agent successfully created",
"cost": create_cost,
} |