| from src.manager.budget_manager import BudgetManager |
| from src.manager.agent_manager import AgentManager |
|
|
| __all__ = ['AskAgent'] |
|
|
|
|
| class AskAgent(): |
| dependencies = ["ollama==0.4.7", |
| "pydantic==2.11.1", |
| "pydantic_core==2.33.0"] |
|
|
| inputSchema = { |
| "name": "AskAgent", |
| "description": "Asks an AI agent a question and gets a response. The agent must be created using the AgentCreator tool before using this tool.", |
| "parameters": { |
| "type": "object", |
| "properties": { |
| "agent_name": { |
| "type": "string", |
| "description": "Name of the AI agent that is to be asked a question. This name cannot have spaces or special characters. It should be a single word.", |
| }, |
| "prompt": { |
| "type": "string", |
| "description": "This is the prompt that will be used to ask the agent a question. It should be a string that describes the question to be asked.", |
| } |
| }, |
| "required": ["agent_name", "prompt"], |
| } |
| } |
|
|
| def run(self, **kwargs): |
| print("Asking agent a question") |
|
|
| agent_name = kwargs.get("agent_name") |
| prompt = kwargs.get("prompt") |
| agent_manger = AgentManager() |
|
|
| try: |
| agent_response, remaining_resource_budget, remaining_expense_budget = agent_manger.ask_agent(agent_name=agent_name, prompt=prompt) |
| except ValueError as e: |
| return { |
| "status": "error", |
| "message": f"Error occurred: {str(e)}", |
| "output": None |
| } |
|
|
| print("Agent response", agent_response) |
| return { |
| "status": "success", |
| "message": "Agent has replied to the given prompt", |
| "output": agent_response, |
| "remaining_resource_budget": remaining_resource_budget, |
| "remaining_expense_budget": remaining_expense_budget |
| } |
|
|
