for {
    res := callLLM(ctx) // vertex: logic nghiệp vụ
    if res.ToolsCalling { // edge: luồng điều khiển
        ctx = executeTools(res.ToolsCalling) // vertex: logic nghiệp vụ
    }
    if res.End { // edge: luồng điều khiển
        return
    }
}

type AddToolParams struct {
    Num1 float64 `json:"num1" jsonschema_description:"First number to add"`
    Num2 float64 `json:"num2" jsonschema_description:"Second number to add"`
}

type AddResult struct {
    llm.BaseLLMToolResult
    Sum float64 `json:"sum" jsonschema_description:"Sum of the two numbers"`
}

addTool := llm.NewLLMTool(
    llm.WithLLMToolName("add"),
    llm.WithLLMToolDescription("Adds two numbers together"),
    llm.WithLLMToolParametersSchema[AddToolParams](),
    llm.WithLLMToolCall(func(callID string, params AddToolParams) (AddResult, error) {
        return AddResult{
            BaseLLMToolResult: llm.BaseLLMToolResult{ID: callID},
            Sum:              params.Num1 + params.Num2,
        }, nil
    }),
)

calculatorAgent, err := agent.NewAgent(
    agent.WithName[CalculatorResult]("calculator"),
    agent.WithLLMConfig[CalculatorResult](llmConfig),
    agent.WithBehavior[CalculatorResult]("Use the add tool to calculate sums. Do not calculate manually."),
    agent.WithTool[CalculatorResult]("add", addTool),
    agent.WithToolLimit[CalculatorResult]("add", 5),
)

var systemPromptTemplate = NewPrompt(`
You are an agent that implements the ReAct
(Reasoning-Action-Observation) pattern to solve tasks through systematic thinking and tool usage.
## REASONING PROTOCOL
Before EVERY action:
1. **THINK**: State your reasoning for the next step
2. **ACT**: Execute the appropriate tool with complete parameters
3. **OBSERVE**: Analyze the results and their implications
Always maintain explicit reasoning chains. Your thoughts should be visible and logical.
## EXECUTION CONTEXT
TOOLS AVAILABLE TO USE:{{.tools}}
CURRENT TOOLS USAGE:{{.tools_usage}}
TOOLS USAGE LIMITS:{{.calling_limits}}
## AGENT BEHAVIOR
<BEHAVIOR>{{.behavior}}</BEHAVIOR>
`)

agent.WithLLMConfig[HashResult](llm.LLMConfig{
    Type:        llm.LLMTypeOpenAI,
    APIKey:      apiKey,
    Model:       "gpt-4o",
    Temperature: 0.0,
})