Laravel AI Agents: Streaming, Token Budgets & Typed Output | Mohamed Said [ ![Mohamed Said](https://cdn.msaied.com/01KT78WE565VEMM3PSNQAAB0MH.png) Mohamed Said Laravel Backend Engineer ](https://msaied.com) [ Home ](https://msaied.com) [ Projects ](https://msaied.com/projects) [ Articles ](https://msaied.com/articles) [ Certificates ](https://msaied.com/certificates) [ Contact ](https://msaied.com#contact-section) [ ](https://github.com/EG-Mohamed) [ Home ](https://msaied.com) [ Projects ](https://msaied.com/projects) [ Articles ](https://msaied.com/articles) [ Certificates ](https://msaied.com/certificates) [ Contact ](https://msaied.com#contact-section) [ home ](https://msaied.com) [ articles ](https://msaied.com/articles) Production AI Agents in Laravel: Streaming, Token Budgets, and Structured Output Contracts On this page 1. [ The Gap Between Demo and Production ](#the-gap-between-demo-and-production) 2. [ 1. Streaming Responses Over SSE ](#1-streaming-responses-over-sse) 3. [ 2. Token Budget Middleware ](#2-token-budget-middleware) 4. [ 3. Structured Output Contracts ](#3-structured-output-contracts) 5. [ Key Takeaways ](#key-takeaways) ![Production AI Agents in Laravel: Streaming, Token Budgets, and Structured Output Contracts](https://cdn.msaied.com/385/7cc838eef4b97e19a20cbbfd52799f52.png) #laravel #ai #llm #php Production AI Agents in Laravel: Streaming, Token Budgets, and Structured Output Contracts ============================================================================================ 7 Jul 2026 4 min read ![Mohamed Said](https://cdn.msaied.com/01KT78WE565VEMM3PSNQAAB0MJ.jpg) Mohamed Said Table of contents 1. [ 01 The Gap Between Demo and Production ](#the-gap-between-demo-and-production) 2. [ 02 1. Streaming Responses Over SSE ](#1-streaming-responses-over-sse) 3. [ 03 2. Token Budget Middleware ](#2-token-budget-middleware) 4. [ 04 3. Structured Output Contracts ](#3-structured-output-contracts) 5. [ 05 Key Takeaways ](#key-takeaways) The Gap Between Demo and Production ----------------------------------- Most Laravel AI tutorials stop at `Http::post('https://api.openai.com/v1/chat/completions', [...])`. That works for a demo. It falls apart in production when a single runaway prompt burns your monthly token budget in an afternoon, a slow model response blocks a PHP-FPM worker for 45 seconds, or a hallucinated JSON blob crashes your downstream pipeline. This article covers three concrete patterns that close that gap: **streaming with SSE**, **token budget middleware**, and **structured output contracts with readonly DTOs**. --- 1. Streaming Responses Over SSE ------------------------------- OpenAI's streaming API sends `text/event-stream` chunks. Laravel's `StreamedResponse` lets you forward them to the browser without buffering the entire completion in memory. ```php // routes/web.php Route::get('/chat/stream', ChatStreamController::class); // app/Http/Controllers/ChatStreamController.php final class ChatStreamController { public function __invoke(ChatRequest $request, AgentService $agent): StreamedResponse { return response()->stream( function () use ($request, $agent) { foreach ($agent->stream($request->validated('message')) as $chunk) { echo "data: {$chunk}\n\n"; ob_flush(); flush(); } echo "data: [DONE]\n\n"; }, 200, [ 'Content-Type' => 'text/event-stream', 'X-Accel-Buffering' => 'no', // critical for nginx 'Cache-Control' => 'no-cache', ] ); } } ``` Inside `AgentService::stream()`, use a generator that reads the chunked HTTP response line by line: ```php public function stream(string $prompt): \Generator { $response = Http::withToken(config('services.openai.key')) ->withOptions(['stream' => true]) ->post('https://api.openai.com/v1/chat/completions', [ 'model' => 'gpt-4o', 'stream' => true, 'messages' => [['role' => 'user', 'content' => $prompt]], ]); $body = $response->toPsrResponse()->getBody(); while (! $body->eof()) { $line = trim($body->read(512)); if (str_starts_with($line, 'data: ') && $line !== 'data: [DONE]') { $data = json_decode(substr($line, 6), true); $content = $data['choices'][0]['delta']['content'] ?? ''; if ($content !== '') { yield $content; } } } } ``` > **Note:** Set `fastcgi_buffering off` in nginx and ensure PHP-FPM output buffering is disabled for the relevant location block. --- 2. Token Budget Middleware -------------------------- Token budgets belong at the pipeline layer, not scattered across controllers. A dedicated middleware class can inspect the request, estimate prompt tokens, and reject or downgrade the model before the HTTP call is made. ```php final class EnforceTokenBudget { // Rough heuristic: 1 token ≈ 4 chars private const CHARS_PER_TOKEN = 4; private const HARD_LIMIT = 8_000; public function handle(AgentPayload $payload, Closure $next): mixed { $estimated = (int) ceil( strlen($payload->systemPrompt . $payload->userMessage) / self::CHARS_PER_TOKEN ); if ($estimated > self::HARD_LIMIT) { throw new TokenBudgetExceededException($estimated, self::HARD_LIMIT); } // Downgrade model for large-but-acceptable prompts if ($estimated > 4_000) { $payload = $payload->withModel('gpt-4o-mini'); } return $next($payload); } } ``` Wire it into a custom `AgentPipeline`: ```php $result = app(Pipeline::class) ->send(new AgentPayload($system, $user)) ->through([ EnforceTokenBudget::class, SanitizeUserInput::class, InjectRagContext::class, ]) ->thenReturn(); ``` --- 3. Structured Output Contracts ------------------------------ OpenAI's `response_format` with `json_schema` mode guarantees the model returns a specific shape. Pair it with a PHP 8.3 readonly DTO and a custom cast for end-to-end type safety. ```php readonly final class ProductSummary { public function __construct( public string $name, public string $oneLinePitch, /** @var list */ public array $keyFeatures, public int $estimatedPriceUsd, ) {} public static function fromArray(array $data): self { return new self( name: $data['name'], oneLinePitch: $data['one_line_pitch'], keyFeatures: $data['key_features'], estimatedPriceUsd: $data['estimated_price_usd'], ); } } ``` Pass the schema to OpenAI: ```php $response = Http::withToken(config('services.openai.key')) ->post('https://api.openai.com/v1/chat/completions', [ 'model' => 'gpt-4o', 'response_format' => [ 'type' => 'json_schema', 'json_schema' => [ 'name' => 'product_summary', 'strict' => true, 'schema' => [ 'type' => 'object', 'properties' => [ 'name' => ['type' => 'string'], 'one_line_pitch' => ['type' => 'string'], 'key_features' => ['type' => 'array', 'items' => ['type' => 'string']], 'estimated_price_usd' => ['type' => 'integer'], ], 'required' => ['name', 'one_line_pitch', 'key_features', 'estimated_price_usd'], 'additionalProperties' => false, ], ], ], 'messages' => [['role' => 'user', 'content' => $prompt]], ]); $summary = ProductSummary::fromArray( json_decode($response->json('choices.0.message.content'), true) ); ``` Because `strict: true` is set, OpenAI will never return extra keys or omit required ones. Your `fromArray` factory becomes a safe assertion, not defensive guesswork. --- Key Takeaways ------------- - Use `response()->stream()` with a generator and `X-Accel-Buffering: no` for true SSE streaming in Laravel. - Enforce token budgets in a pipeline middleware layer, not inside controllers or service methods. - `json_schema` with `strict: true` eliminates hallucinated keys; map the response to a readonly DTO immediately. - Estimate prompt tokens early (chars ÷ 4 is good enough for budget checks) to avoid surprise costs. - Model downgrade logic belongs in the pipeline, keeping your agent service stateless and testable. Found this useful? [ ](https://twitter.com/intent/tweet?url=https%3A%2F%2Fmsaied.com%2Farticles%2Fproduction-ai-agents-in-laravel-streaming-token-budgets-and-structured-output-contracts-2&text=Production+AI+Agents+in+Laravel%3A+Streaming%2C+Token+Budgets%2C+and+Structured+Output+Contracts) [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fmsaied.com%2Farticles%2Fproduction-ai-agents-in-laravel-streaming-token-budgets-and-structured-output-contracts-2) Frequently Asked Questions ---------------------------- 3 questions Q01 Does Laravel Octane break SSE streaming? Octane's Swoole driver handles streaming natively via its response object. Replace `response()->stream()` with `$server->push()` patterns or use the FrankenPHP driver, which supports streamed responses out of the box. Test with a real client; Octane workers do not buffer the way PHP-FPM does. Q02 How accurate is the chars-divided-by-4 token estimate? It is a conservative heuristic suitable for budget guards. For precise counts, use the tiktoken-php library or call OpenAI's tokenizer endpoint. The heuristic intentionally over-counts to provide a safety margin before hitting hard model context limits. Q03 Can I use structured output with streaming enabled simultaneously? Yes. OpenAI supports both `stream: true` and `response_format: json_schema` together. The JSON is streamed as partial tokens, so you must buffer the full stream before parsing the DTO. Only enable streaming for structured output if you need progress indication; otherwise a standard blocking call is simpler. 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