neuron-debugger

name

neuron-debugger

description

Debug and monitor Neuron AI applications with Inspector APM, event observability, logging, and performance analysis. Use this skill whenever the user mentions debugging, monitoring, observability, performance analysis, tracing, Inspector, or needs to understand why an agent is behaving a certain way. Also trigger for tasks involving agent execution timeline, tool call inspection, response quality issues, latency problems, or general troubleshooting of Neuron AI applications.

Neuron AI Debugger

This skill helps you debug and monitor Neuron AI applications using Inspector APM and the framework's observability system.

Inspector APM Integration

Inspector provides deep insights into agent execution, helping you understand:

• Why the model took certain decisions
• What data the model reacted to
• Timeline of all operations (LLM calls, tool usage, vector search)
• Performance bottlenecks and latency

Setup

1. Get an Inspector account at https://inspector.dev
2. Set the ingestion key in your environment:

# .env file
INSPECTOR_INGESTION_KEY=your_ingestion_key_here

3. Agent execution is automatically tracked - no code changes needed!

Viewing Execution Timeline

After running an agent, visit your Inspector dashboard to see:

[AI Inference] → [Tool Call: search_database] → [AI Inference] → [Response]
     ↓                    ↓                        ↓
  850ms               1.2s                     920ms

Each segment shows:

• Duration and timing
• Input/output data
• Errors if any occurred
• Metadata (model used, tokens, etc.)

Event System Observability

Neuron uses a static EventBus for monitoring all framework events.

Event Emission

All components emit events automatically:

use NeuronAI\Observability\EventBus;

// Workflows emit events like:
// - 'workflow-start'
// - 'workflow-resume'
// - 'workflow-end'
// - 'workflow-node-start'
// - 'workflow-node-end'
// - 'middleware-before-start'
// - 'middleware-after-end'
// - 'tool-calling'
// - 'tool-called'
// - 'rag-retrieving'
// - 'rag-retrieved'
// - 'inference-start'
// - 'inference-end'

Custom Observers

use NeuronAI\Observability\ObserverInterface;

class CustomObserver implements ObserverInterface
{
    public function handle(string $event, object $source, mixed $data): void
    {
        // Handle events
        echo "Event: {$event}\n";
        echo "Source: " . $source::class . "\n";
        var_dump($data);
    }
}

Registering Observers

use NeuronAI\Observability\EventBus;

// Register globally
EventBus::observe(new CustomObserver());

// Or register via workflow/agent
$workflow->observe(new CustomObserver());

Extending InspectorObserver

Create custom observers that maintain all default tracking:

use NeuronAI\Observability\InspectorObserver;

class MyInspectorObserver extends InspectorObserver
{
    protected array $methodsMap = [
        // Include parent mappings
        ...parent::$methodsMap,

        // Add custom event mappings
        'my-custom-event' => 'handleCustomEvent',
    ];

    public function handleCustomEvent(object $source, string $event, mixed $data): void
    {
        $segment = $this->inspector->addSegment('custom', $event);

        // Custom tracking logic
        $this->trackCustomMetrics($data);

        $segment->end();
    }

    private function trackCustomMetrics(mixed $data): void
    {
        // Your custom metrics
    }
}

// Register before any execution
EventBus::setDefaultObserver(MyInspectorObserver::instance());

Common Debugging Scenarios

Agent Not Using Tools

Symptoms: Agent ignores available tools

Diagnosis Steps:

1. Check Inspector timeline - are tool calls being made?
2. Verify tool descriptions are clear and specific
3. Check if tool properties are correctly defined
4. Review agent instructions - are tools mentioned?

// Add explicit tool instruction
protected function instructions(): string
{
    return (string) new SystemPrompt(
        background: [
            "You have access to database tools to query user data.",
            "Use the search_user tool when asked about users.",
        ]
    );
}

Slow Agent Responses

Symptoms: High latency, slow responses

Diagnosis Steps:

1. Check Inspector timeline - identify slow segments

2. Common bottlenecks:

   • LLM inference: Check model choice, token count
   • Tool execution: Database queries, API calls
   • Vector search: Large collections, slow embeddings

3. Optimization strategies:

   • Enable parallel tool calls
   • Optimize database queries
   • Use smaller/faster embedding models

// Enable parallel tool execution
$agent->parallelToolCalls(true);

Poor Response Quality

Symptoms: Hallucinations, irrelevant responses

Diagnosis Steps:

1. Check Inspector - what context was provided?

2. Review LLM calls:

   • System prompt quality
   • Context window usage
   • RAG retrieval results

3. Common fixes:

   • Improve system prompt
   • Increase RAG k value (retrieve more documents)
   • Add reranking
   • Use better embedding models

// Improve retrieval quality
$rag->addPostProcessor(new RerankProcessor(
    reranker: new CohereReranker($apiKey),
    topK: 5
));

// Better system prompt
protected function instructions(): string
{
    return (string) new SystemPrompt(
        background: [
            "You are a helpful assistant answering questions",
            "about our product using only the provided context.",
        ],
        steps: [
            "Never make up information.",
            "If you don't know, say so clearly.",
            "Cite sources when possible.",
        ]
    );
}

Tools Not Executing

Symptoms: Agent tries to call tools but fails

Diagnosis Steps:

1. Check Inspector timeline - see error details

2. Verify tool configuration:

   • Property types match
   • Required parameters provided
   • Dependencies injected

3. Add error handling:

class MyTool extends Tool
{
    public function execute(array $arguments): mixed
    {
        try {
            // Tool logic
            return $result;
        } catch (\Exception $e) {
            // Return error in a format the LLM can understand
            return [
                'error' => $e->getMessage(),
                'type' => get_class($e),
                'hint' => 'Check your parameters and try again.',
            ];
        }
    }
}

Logging

PSR-3 Logger Integration

use NeuronAI\Observability\LogObserver;
use Monolog\Logger;
use Monolog\Handler\StreamHandler;

$logger = new Logger('neuron');
$logger->pushHandler(new StreamHandler('php://stdout'));

$agent->observe(new LogObserver($logger));

Custom Logger

use NeuronAI\Observability\ObserverInterface;

class FileLogger implements ObserverInterface
{
    private $file;

    public function __construct(string $filepath)
    {
        $this->file = fopen($filepath, 'a');
    }

    public function handle(string $event, object $source, mixed $data): void
    {
        $timestamp = date('Y-m-d H:i:s');
        $log = "[{$timestamp}] {$event} from {$source::class}\n";
        fwrite($this->file, $log);
    }

    public function __destruct()
    {
        fclose($this->file);
    }
}

Testing Debugging Scenarios

Test Response with Mock Provider

use PHPUnit\Framework\TestCase;

class AgentTest extends TestCase
{
    public function testAgentResponseQuality(): void
    {
        // Use fake provider for deterministic testing
        $agent = new MyAgent(new FakeAIProvider([
            'expected_response' => 'Helpful answer here'
        ]));

        $response = $agent->chat(
            new UserMessage('Test question')
        )->getMessage();

        $this->assertStringContainsString('key information', $response->getContent());
    }
}

Test Tool Execution

public function testToolExecution(): void
{
    $tool = new MyTool();
    $result = $tool->execute(['param' => 'value']);

    $this->assertIsArray($result);
    $this->assertArrayHasKey('result', $result);
}

Production Error Analysis

Inspector provides detailed error analysis:

1. Error Summary: Frequency, severity, affected code
2. Stack Traces: Full call chain with framework code
3. Context: Input data, state at time of error
4. Patterns: Repeated issues, common failure modes

Tracing Node Execution

use NeuronAI\Workflow\EventBus;
use NeuronAI\Workflow\NodeInterface;

class TracingObserver implements ObserverInterface
{
    public function handle(string $event, object $source, mixed $data): void
    {
        if ($source instanceof NodeInterface) {
            echo "Node {$source::class}: {$event}\n";
        }
    }
}

EventBus::observe(new TracingObserver());

Exporting Workflows for Visualization

use NeuronAI\Workflow\Exporter\MermaidExporter;

$workflow->setExporter(new MermaidExporter());
$diagram = $workflow->export();

file_put_contents('workflow_diagram.mmd', $diagram);

Debugging Checklist

When troubleshooting:

• Is Inspector configured with valid ingestion key?
• Can you see the execution in Inspector dashboard?
• Are errors shown in the timeline?
• What was the LLM prompt and response?
• Were tools called, and what were the results?
• Is the response quality poor or execution failing?
• Check logs for additional context
• Verify tool property types match what was sent
• For RAG, check retrieved documents and scores
• Consider adding a custom observer for specific events

Getting Help

If issues persist:

1. Check Inspector - timeline and errors
2. Review logs - application and framework logs
3. Create minimal reproduction - simplify the agent
4. Consult Neuron AI documentation - https://docs.neuron-ai.dev/