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Overview

This example uses InferenceRuntime directly and shows the Langfuse connection inline. It is useful when you want visibility into the raw LLM call path without the additional StructuredOutput layer. Key concepts:
  • explicit LangfuseConfig / LangfuseHttpTransport / LangfuseExporter setup
  • InferenceRuntime: direct Polyglot runtime for inference calls
  • PolyglotTelemetryProjector: maps inference lifecycle events
  • HttpClientTelemetryProjector: captures transport spans
  • Telemetry::flush(): pushes the final batch to Langfuse

​
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\Langfuse\LangfuseConfig;
use Cognesy\Telemetry\Adapters\Langfuse\LangfuseExporter;
use Cognesy\Telemetry\Adapters\Langfuse\LangfuseHttpTransport;
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-langfuse';
$baseUrl = (string) Env::get('LANGFUSE_BASE_URL', '');
if ($baseUrl === '') {
    throw new RuntimeException('Set LANGFUSE_BASE_URL in .env to run this example.');
}
$publicKey = (string) Env::get('LANGFUSE_PUBLIC_KEY', '');
if ($publicKey === '') {
    throw new RuntimeException('Set LANGFUSE_PUBLIC_KEY in .env to run this example.');
}
$secretKey = (string) Env::get('LANGFUSE_SECRET_KEY', '');
if ($secretKey === '') {
    throw new RuntimeException('Set LANGFUSE_SECRET_KEY in .env to run this example.');
}

$events = new EventDispatcher($serviceName);
$hub = new Telemetry(
    registry: new TraceRegistry(),
    exporter: new LangfuseExporter(
        transport: new LangfuseHttpTransport(new LangfuseConfig(
            baseUrl: $baseUrl,
            publicKey: $publicKey,
            secretKey: $secretKey,
        )),
    ),
);

(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 Langfuse\n";

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