Observability

Monitor your AI applications with tracing, logging, and custom hooks in Orka AI.

Native Observability & LLMOps

Don't fly blind. Orka JS provides deep introspection into every LLM call, tool execution, and RAG retrieval. Monitor latencies, token usage, and errors with simple, powerful hooks.

observability: {
  logLevel: 'info',
  hooks: [{
    onTraceEnd: (trace) => {
      // Direct integration with Datadog/Sentry
      reportMetrics(trace.totalLatencyMs, trace.totalTokens);
    },
    onError: (err, ctx) => alertDevTeam(err)
  }],
}

Token Tracking: Detailed usage per trace for precise cost management.

Latency Profiling: Identify bottlenecks in your RAG or tool chains instantly.

Configuration

observability-config.ts

const orka = createOrka({
  llm: new OpenAIAdapter({ apiKey: '...' }),
  vectorDB: new MemoryVectorAdapter(),
  observability: {
    logLevel: 'info', // 'debug' | 'info' | 'warn' | 'error'
    hooks: [{
      onTraceStart: (trace) => {
        console.log(`🔍 Trace started: ${trace.name}`);
      },
      onTraceEnd: (trace) => {
        console.log(`✅ Done: ${trace.totalLatencyMs}ms, ${trace.totalTokens} tokens`);
      },
      onError: (error, context) => {
        console.error(`❌ Error: ${error.message}`, context);
        // Send to Sentry, Datadog, etc.
      },
    }],
  },
});

Log Levels

Level	Description
debug	Detailed internal operations
info	Normal operations (default)
warn	Potential issues
error	Failures only

Manual Tracing

tracing.ts

// Start a trace
const trace = orka.tracer.startTrace('my-pipeline', {
  userId: 'user-123',
});
 
// Add events
orka.tracer.addEvent(trace.id, {
  type: 'custom',
  name: 'preprocessing',
  startTime: Date.now(),
  endTime: Date.now() + 50,
});
 
// End the trace
const completed = orka.tracer.endTrace(trace.id);
console.log(`Total: ${completed?.totalLatencyMs}ms`);

💡 Production Tip

In production, set logLevel: 'warn' to reduce noise, and use hooks for structured monitoring with Datadog, Sentry, or custom dashboards.