Observability

Observability

All three runtimes provide observability features for production deployments.

Rust: Structured Logging

ai-lib-rust uses the tracing ecosystem:

use tracing_subscriber;

// Enable logging
tracing_subscriber::init();

// All AI-Lib operations emit structured log events
let client = AiClient::new("openai/gpt-4o").await?;

Log levels:

• INFO — Request/response summaries
• DEBUG — Protocol loading, pipeline stages
• TRACE — Individual frames, JSONPath matches

Rust: Call Statistics

Every request returns usage statistics:

let (response, stats) = client.chat()
    .user("Hello")
    .execute_with_stats()
    .await?;

println!("Model: {}", stats.model);
println!("Provider: {}", stats.provider);
println!("Prompt tokens: {}", stats.prompt_tokens);
println!("Completion tokens: {}", stats.completion_tokens);
println!("Total tokens: {}", stats.total_tokens);
println!("Latency: {}ms", stats.latency_ms);

Python: Call Statistics

response, stats = await client.chat() \
    .user("Hello") \
    .execute_with_stats()

print(f"Tokens: {stats.total_tokens}")
print(f"Latency: {stats.latency_ms}ms")

TypeScript: Call Statistics

const { response, stats } = await client
  .chat()
  .user('Hello')
  .executeWithStats();

console.log(`Model: ${stats.model}`);
console.log(`Provider: ${stats.provider}`);
console.log(`Prompt tokens: ${stats.promptTokens}`);
console.log(`Completion tokens: ${stats.completionTokens}`);
console.log(`Total tokens: ${stats.totalTokens}`);
console.log(`Latency: ${stats.latencyMs}ms`);

Python: Metrics (Prometheus)

from ai_lib_python.telemetry import MetricsCollector

metrics = MetricsCollector()

client = await AiClient.builder() \
    .model("openai/gpt-4o") \
    .metrics(metrics) \
    .build()

# After some requests...
prometheus_text = metrics.export_prometheus()

TypeScript: Metrics (Prometheus)

import { MetricsCollector } from '@hiddenpath/ai-lib-ts/telemetry';

const metrics = new MetricsCollector();

const client = await AiClient.builder()
  .model('openai/gpt-4o')
  .metrics(metrics)
  .build();

// After some requests...
const prometheusText = metrics.exportPrometheus();

Tracked metrics:

• ai_lib_requests_total — Request count by model/provider
• ai_lib_request_duration_seconds — Latency histogram
• ai_lib_tokens_total — Token usage by type
• ai_lib_errors_total — Error count by type

Python: Distributed Tracing (OpenTelemetry)

from ai_lib_python.telemetry import Tracer

tracer = Tracer(
    service_name="my-app",
    endpoint="http://jaeger:4317",
)

client = await AiClient.builder() \
    .model("openai/gpt-4o") \
    .tracer(tracer) \
    .build()

TypeScript: Distributed Tracing (OpenTelemetry)

import { Tracer } from '@hiddenpath/ai-lib-ts/telemetry';

const tracer = new Tracer({
  serviceName: 'my-app',
  endpoint: 'http://jaeger:4317',
});

const client = await AiClient.builder()
  .model('openai/gpt-4o')
  .tracer(tracer)
  .build();

Traces include spans for:

• Protocol loading
• Request compilation
• HTTP transport
• Pipeline processing
• Event mapping

Python: Health Monitoring

from ai_lib_python.telemetry import HealthChecker

health = HealthChecker()
status = await health.check()

print(f"Healthy: {status.is_healthy}")
print(f"Details: {status.details}")

TypeScript: Health Monitoring

import { HealthChecker } from '@hiddenpath/ai-lib-ts/telemetry';

const health = new HealthChecker();
const status = await health.check();

console.log(`Healthy: ${status.isHealthy}`);
console.log(`Details: ${status.details}`);

Python: User Feedback

Collect feedback on AI responses:

from ai_lib_python.telemetry import FeedbackCollector

feedback = FeedbackCollector()

# After getting a response
feedback.record(
    request_id=stats.request_id,
    rating=5,
    comment="Helpful response",
)

TypeScript: User Feedback

import { FeedbackCollector } from '@hiddenpath/ai-lib-ts/telemetry';

const feedback = new FeedbackCollector();

// After getting a response
feedback.record({
  requestId: stats.requestId,
  rating: 5,
  comment: 'Helpful response',
});

Resilience Observability

Monitor circuit breaker and rate limiter state:

// Rust
let state = client.circuit_state(); // Closed, Open, HalfOpen
let inflight = client.current_inflight();

# Python
signals = client.signals_snapshot()
print(f"Circuit: {signals.circuit_state}")
print(f"Inflight: {signals.current_inflight}")

// TypeScript
const signals = client.signalsSnapshot();
console.log(`Circuit: ${signals.circuitState}`);
console.log(`Inflight: ${signals.currentInflight}`);