Qwen3 Loop Exit Issue In Loop_for_reflection Example

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Qwen3 Loop Exit Issue in `loop_for_reflection` Example

Hey guys! Let's dive into a common issue encountered while using the loop_for_reflection example with the Qwen3 model: the loop not exiting automatically. This can be a bit puzzling, so let's break it down and see what's going on under the hood. We'll explore why explicitly specifying an exit tool might be necessary and whether it's related to the model itself or how we're using it.

Understanding the Problem

So, you're running the loop_for_reflection example, and you've noticed that the loop just keeps going and going, like the Energizer Bunny! It doesn't stop on its own. You're probably thinking, "What's the deal?" Well, the core issue lies in how the agent determines when to stop reflecting and exit the loop. In many agent-based systems, especially those involving reflection or critique, the agent needs a clear signal or condition to know when its task is complete. Without this, it's like a dog chasing its tail – it just keeps going.

The snippet of code you provided highlights a crucial part of the configuration:

a, err := adk.NewChatModelAgent(context.Background(), &adk.ChatModelAgentConfig{
		Name:        "CritiqueAgent",
		Description: "Critique agent that reviews the main agent's work and provides feedback.",
		Instruction: `You are a critique agent responsible for reviewing the main agent's work.
		Analyze the provided solution for accuracy, completeness, and quality.
		If you find issues or areas for improvement, provide specific feedback.
		If the work is satisfactory, call the 'exit' tool and provide a final summary response.`, 
		Model: model.NewChatModel(),
		// Exit:  endTool, // use default exit tool
		Exit: adk.ExitTool{},
	})

Notice the Exit field in the ChatModelAgentConfig. This field is responsible for defining the tool that the agent uses to signal its completion. If this tool isn't configured properly, or if the model isn't instructed clearly on when to use it, the loop won't know when to stop. Let's delve deeper into why this might be happening and how to fix it.

Why Qwen3 Might Not Be Exiting Automatically

Several factors could contribute to this behavior. Let's consider them:

  1. Model's Understanding of the Exit Condition: The Qwen3 model needs to understand the specific conditions under which it should trigger the exit tool. This understanding comes from the instructions provided in the Instruction field. If the instructions are ambiguous or if the model struggles to recognize when the work is "satisfactory," it might not call the exit tool.

  2. Configuration of the Exit Tool: The Exit field in the agent configuration determines which tool is used to exit the loop. If you're using a default exit tool, it might not be properly configured to work with Qwen3's output. The commented-out line // Exit: endTool, // use default exit tool suggests you might have considered a custom exit tool. The line Exit: adk.ExitTool{} indicates you're using the default adk.ExitTool{} which might not be sophisticated enough for the task.

  3. Specificity of Instructions: The instructions provided to the CritiqueAgent might not be specific enough. For example, the phrase "If the work is satisfactory, call the 'exit' tool and provide a final summary response" relies on the model's interpretation of "satisfactory." This can be subjective and may vary between model runs. More precise conditions, such as "If no issues are found after three reviews, call the 'exit' tool," can improve the model's ability to recognize the exit condition.

  4. Model Limitations: It's also possible that the Qwen3 model, like any language model, has limitations in its ability to perfectly follow instructions or consistently recognize complex conditions. This doesn't mean the model is bad; it just means we need to be extra clear and precise in our instructions and configurations.

Specifying the Exit Tool Explicitly

Explicitly specifying the exit tool can often resolve the issue. Here's why:

  • Clarity: It removes any ambiguity about which tool the agent should use to signal completion.
  • Customization: It allows you to tailor the exit tool to the specific requirements of your task and the capabilities of the Qwen3 model.
  • Control: It gives you more control over the exit condition. You can define exactly what constitutes a successful completion of the loop.

Example of Explicitly Specifying an Exit Tool

Let's say you want the agent to exit after three reviews if no issues are found. You could define a custom exit tool like this:

package main

import (
	"context"
	"fmt"

	"github.com/ConnectAI-E/appagent-sdk/adk"
	"github.com/ConnectAI-E/appagent-sdk/model"
)

// CustomExitTool defines a custom exit tool.
type CustomExitTool struct {
	ReviewCount int
	MaxReviews  int
}

// NewCustomExitTool creates a new CustomExitTool instance.
func NewCustomExitTool(maxReviews int) *CustomExitTool {
	return &CustomExitTool{
		ReviewCount: 0,
		MaxReviews:  maxReviews,
	}
}

// Call is the method that gets called when the tool is executed.
func (t *CustomExitTool) Call(ctx context.Context, args map[string]interface{}) (string, error) {
	// Increment the review count.
	
t.ReviewCount++

	// Check if the maximum number of reviews has been reached.
	
if t.ReviewCount >= t.MaxReviews {
		// Check if any issues were found in the reviews (you'd need to integrate
		// this logic based on how issues are reported in your system).
		
noIssues := true // Replace with actual issue checking logic

		
if noIssues {
			
return "Exiting: No issues found after " + fmt.Sprintf("%d", t.ReviewCount) + " reviews.", nil
		}
	}

	// If the maximum number of reviews hasn't been reached or issues were found,
	// return an empty string to continue the loop.
	
return "", nil
}

// Definition provides the tool definition.
func (t *CustomExitTool) Definition() adk.ToolDefinition {
	return adk.ToolDefinition{
		Name:        "custom_exit",
		Description: "Exits the loop after a specified number of reviews if no issues are found.",
		Parameters:  map[string]interface{}{},
	}
}

func main() {
	// Initialize the Chat Model
	chatModel := model.NewChatModel()

	// Create a new instance of the custom exit tool.
	customExitTool := NewCustomExitTool(3)

	// Define the agent configuration
	config := &adk.ChatModelAgentConfig{
		Name:        "CritiqueAgent",
		Description: "Critique agent that reviews the main agent's work and provides feedback.",
		Instruction: `You are a critique agent responsible for reviewing the main agent's work. Analyze the provided solution for accuracy, completeness, and quality. If you find issues or areas for improvement, provide specific feedback. After each review, call the 'custom_exit' tool. The loop will exit after 3 reviews if no issues are found.`, 
		Model:       chatModel,
		Exit:        customExitTool,
	}

	// Create a new chat model agent
	agent, err := adk.NewChatModelAgent(context.Background(), config)
	
if err != nil {
		fmt.Printf("Error creating agent: %v", err)
		return
	}

	// Simulate agent execution (replace with your actual loop logic)
	
for i := 0; i < 5; i++ {
		
fmt.Printf("Review %d:\n", i+1)
		// Simulate calling the agent (replace with your actual agent call logic)
		
result, err := agent.Call(context.Background(), map[string]interface{}{})
		
if err != nil {
			fmt.Printf("Error calling agent: %v\n", err)
			break
		}
		
fmt.Printf("Agent Result: %s\n", result)

		// Check if the agent signaled to exit
		
if result != "" {
			
fmt.Println("Exiting loop.")
			
break
		}
	}
}

Explanation:

  • CustomExitTool: This struct holds the review count and the maximum number of reviews.
  • NewCustomExitTool: This function creates a new instance of the CustomExitTool.
  • Call: This is the core logic of the tool. It increments the review count and checks if the maximum number of reviews has been reached. It also (and this is crucial) checks if any issues were found during the reviews. If no issues were found and the maximum number of reviews has been reached, it returns a message indicating that the loop should exit. If either of those conditions isn't met, it returns an empty string, signaling the loop to continue.
  • Definition: This provides the tool's metadata, including its name and description.
  • Agent Configuration: In the agent configuration, the Exit field is set to an instance of the CustomExitTool, and the Instruction is updated to tell the agent to call the "custom_exit" tool after each review.

Key Improvements:

  • Clear Exit Condition: The exit condition is now explicitly defined: exit after three reviews only if no issues are found.
  • Specific Instructions: The agent is explicitly instructed to call the custom_exit tool after each review.

Is It a Model Problem or a Usage Problem?

The question of whether this is a model problem or a usage problem is a good one. The answer is often "it's a bit of both!"

  • Usage Problem: In many cases, the issue stems from how the agent is configured and instructed. Ambiguous instructions, a lack of a clear exit condition, or an improperly configured exit tool can all lead to the loop not exiting automatically. This is why explicitly defining the exit tool and providing precise instructions often solves the problem.

  • Model Problem: However, it's also true that the model's capabilities play a role. If the model struggles to understand the instructions, or if it's not very good at recognizing the exit condition, it might not call the exit tool even with clear instructions. Different models will have varying levels of performance in this regard. It is important to test and experiment with different models and instructions to find what works best for your specific use case.

Troubleshooting Tips

Here are some additional troubleshooting tips to help you get the loop_for_reflection example working correctly with Qwen3:

  1. Simplify Instructions: Start with very simple instructions and gradually increase the complexity. This helps you isolate the source of the problem.

  2. Log Everything: Log all inputs, outputs, and tool calls. This gives you a detailed record of what's happening during the loop and can help you identify where things are going wrong.

  3. Experiment with Different Models: Try using a different chat model to see if the problem persists. If the loop exits automatically with a different model, it suggests that the issue might be related to Qwen3's specific capabilities.

  4. Monitor Resource Usage: Ensure that the Qwen3 model and your application have sufficient resources (CPU, memory) to operate effectively. Resource exhaustion can sometimes lead to unexpected behavior.

  5. Check for Errors: Carefully examine the logs for any error messages or exceptions. These can provide valuable clues about the cause of the problem.

Conclusion

The issue of the loop_for_reflection example not exiting automatically with Qwen3 often boils down to the clarity of instructions and the configuration of the exit tool. By explicitly specifying the exit tool and providing precise instructions, you can significantly improve the model's ability to recognize the exit condition and terminate the loop gracefully. While the model's capabilities play a role, focusing on clear and specific instructions is usually the key to success. Remember to test, experiment, and carefully examine the logs to diagnose and resolve any issues you encounter. Good luck, and happy coding!