Llm telemetry logfire - Instructor for PHP

Overview

This example uses InferenceRuntime directly and shows the Logfire connection inline. It is useful when you want visibility into the raw LLM call path without the additional StructuredOutput layer. Key concepts:

explicit LogfireConfig / LogfireExporter setup
InferenceRuntime: direct Polyglot runtime for inference calls
PolyglotTelemetryProjector: maps inference lifecycle events
HttpClientTelemetryProjector: captures transport spans
Telemetry::flush(): pushes the final batch to Logfire

Example

<?php
require 'examples/boot.php';

use Cognesy\Config\Env;
use Cognesy\Events\Dispatchers\EventDispatcher;
use Cognesy\Http\Telemetry\HttpClientTelemetryProjector;
use Cognesy\Messages\Messages;
use Cognesy\Polyglot\Inference\Inference;
use Cognesy\Polyglot\Inference\InferenceRuntime;
use Cognesy\Polyglot\Inference\LLMProvider;
use Cognesy\Polyglot\Telemetry\PolyglotTelemetryProjector;
use Cognesy\Telemetry\Adapters\Logfire\LogfireConfig;
use Cognesy\Telemetry\Adapters\Logfire\LogfireExporter;
use Cognesy\Telemetry\Application\Registry\TraceRegistry;
use Cognesy\Telemetry\Application\Telemetry;
use Cognesy\Telemetry\Application\Projector\CompositeTelemetryProjector;
use Cognesy\Telemetry\Application\Projector\RuntimeEventBridge;

$serviceName = 'examples.b03.telemetry-logfire';
$token = (string) Env::get('LOGFIRE_TOKEN', '');
if ($token === '') {
    throw new RuntimeException('Set LOGFIRE_TOKEN in .env to run this example.');
}
$endpoint = (string) Env::get('LOGFIRE_OTLP_ENDPOINT', '');
if ($endpoint === '') {
    throw new RuntimeException('Set LOGFIRE_OTLP_ENDPOINT in .env to run this example.');
}

$events = new EventDispatcher($serviceName);
$hub = new Telemetry(
    registry: new TraceRegistry(),
    exporter: new LogfireExporter(new LogfireConfig(
        endpoint: rtrim($endpoint, '/'),
        serviceName: $serviceName,
        headers: ['Authorization' => $token],
    )),
);

(new RuntimeEventBridge(new CompositeTelemetryProjector([
    new PolyglotTelemetryProjector($hub),
    new HttpClientTelemetryProjector($hub),
])))->attachTo($events);

$runtime = InferenceRuntime::fromProvider(
    provider: LLMProvider::using('openai'),
    events: $events,
);

$response = Inference::fromRuntime($runtime)
    ->with(
        messages: Messages::fromString('Summarize why observability matters for LLM applications in exactly 3 bullet points.'),
        options: ['max_tokens' => 180],
    )
    ->response();

$hub->flush();

echo "Response:\n";
echo $response->content() . "\n\n";
if ($response->usage() !== null) {
    echo "Tokens: {$response->usage()->inputTokens} in / {$response->usage()->outputTokens} out\n";
}
echo "Telemetry: flushed to Logfire\n";

assert($response->content() !== '');
?>

​Overview

​Example

Overview

Example