hashiruAI / src /tools /default_tools /agent_cost_manager.py
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Fixed llama to be 3B
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__all__ = ['AgentCostManager']
class AgentCostManager():
dependencies = []
inputSchema = {
"name": "AgentCostManager",
"description": "Retrieves the cost of creating and invoking an agent. Please make sure to use this before creating an agent.",
"parameters": {
"type": "object",
"properties": {},
"required": [],
}
}
costs = {
"llama3.2": {
"description": "3 Billion parameter model",
"create_resource_cost": 50,
"invoke_resource_cost": 30,
},
"mistral": {
"description": "7 Billion parameter model",
"create_resource_cost": 75,
"invoke_resource_cost": 40,
},
"deepseek-r1": {
"description": "7 Billion reasoning model",
"create_resource_cost": 28,
"invoke_resource_cost": 35,
},
"gemini-2.5-flash-preview-04-17": {
"description": "Adaptive thinking, cost efficiency",
"create_expense_cost": 0.005,
"invoke_expense_cost": 0.00017
},
"gemini-2.5-pro-preview-03-25": {
"description": "Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more",
"create_expense_cost": 0.005,
"invoke_expense_cost": 0.0001275
},
"gemini-2.0-flash": {
"description": "Next generation features, speed, thinking, realtime streaming, and multimodal generation",
"create_expense_cost": 0.005,
"invoke_expense_cost": 0.00017
},
"gemini-2.0-flash-lite": {
"description": "Cost efficiency and low latency",
"create_expense_cost": 0.005,
"invoke_expense_cost": 0.00017
},
"gemini-1.5-flash": {
"description": "Fast and versatile performance across a diverse variety of tasks",
"create_expense_cost": 0.005,
"invoke_expense_cost": 0.00017
},
"gemini-1.5-flash-8b": {
"description": "High volume and lower intelligence tasks",
"create_expense_cost": 0.005,
"invoke_expense_cost": 0.00017
},
"gemini-1.5-pro": {
"description": "Complex reasoning tasks requiring more intelligence",
"create_expense_cost": 0.005,
"invoke_expense_cost": 0.0001275
},
"gemini-2.0-flash-live-001": {
"description": "Low-latency bidirectional voice and video interactions",
"create_expense_cost": 0.005,
"invoke_expense_cost": 0.000635
}
}
def get_costs(self):
return self.costs
def run(self, **kwargs):
return {
"status": "success",
"message": "Cost of creating and invoking an agent",
"output": self.costs,
}