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 Problem With Naive AI Integration ](#the-problem-with-naive-ai-integration) 2. [ Streaming Responses to the Browser ](#streaming-responses-to-the-browser) 3. [ Token Budget Guard ](#token-budget-guard) 4. [ Structured Output Contracts ](#structured-output-contracts) 5. [ Keeping the Domain Clean ](#keeping-the-domain-clean) 6. [ Takeaways ](#takeaways) ![Production AI Agents in Laravel: Streaming, Token Budgets, and Structured Output Contracts](https://cdn.msaied.com/262/4220ef4ee3875717c6384f298d891a3f.png) #laravel #ai #llm #php Production AI Agents in Laravel: Streaming, Token Budgets, and Structured Output Contracts ============================================================================================ 22 Jun 2026 3 min read ![Mohamed Said](https://cdn.msaied.com/01KT78WE565VEMM3PSNQAAB0MJ.jpg) Mohamed Said Table of contents 1. [ 01 The Problem With Naive AI Integration ](#the-problem-with-naive-ai-integration) 2. [ 02 Streaming Responses to the Browser ](#streaming-responses-to-the-browser) 3. [ 03 Token Budget Guard ](#token-budget-guard) 4. [ 04 Structured Output Contracts ](#structured-output-contracts) 5. [ 05 Keeping the Domain Clean ](#keeping-the-domain-clean) 6. [ 06 Takeaways ](#takeaways) The Problem With Naive AI Integration ------------------------------------- Most Laravel + LLM tutorials stop at `Http::post('https://api.openai.com/v1/chat/completions', [...])` and call it done. That works for demos. In production you need three things the tutorials skip: 1. **Streaming** — users shouldn't stare at a spinner for 8 seconds. 2. **Token budgets** — unbounded prompts destroy your billing and latency SLAs. 3. **Structured output contracts** — raw JSON strings from an LLM are not domain objects. Let's solve all three without a heavy third-party SDK. --- Streaming Responses to the Browser ---------------------------------- OpenAI's `stream: true` returns server-sent events. Laravel's `StreamedResponse` pipes them straight to the client. ```php // app/Http/Controllers/AgentController.php public function stream(Request $request): StreamedResponse { $prompt = $request->validated()['prompt']; return response()->stream(function () use ($prompt) { $stream = Http::withToken(config('services.openai.key')) ->withOptions(['stream' => true]) ->post('https://api.openai.com/v1/chat/completions', [ 'model' => 'gpt-4o-mini', 'stream' => true, 'messages' => [['role' => 'user', 'content' => $prompt]], ])->toPsrResponse()->getBody(); while (! $stream->eof()) { $line = trim($stream->read(512)); if (str_starts_with($line, 'data: ')) { $payload = substr($line, 6); if ($payload === '[DONE]') break; $delta = json_decode($payload, true)['choices'][0]['delta']['content'] ?? ''; echo "data: {$delta}\n\n"; ob_flush(); flush(); } } }, 200, ['Content-Type' => 'text/event-stream', 'X-Accel-Buffering' => 'no']); } ``` The `X-Accel-Buffering: no` header is essential when Nginx sits in front — without it, Nginx buffers the whole response. --- Token Budget Guard ------------------ Never let user input dictate prompt size. Enforce a budget before the HTTP call. ```php // app/AI/TokenBudget.php final class TokenBudget { private const CHARS_PER_TOKEN = 4; // rough heuristic private const MAX_INPUT_TOKENS = 1_500; public static function enforce(string $text): string { $limit = self::MAX_INPUT_TOKENS * self::CHARS_PER_TOKEN; if (strlen($text) post('https://api.openai.com/v1/chat/completions', [ 'model' => 'gpt-4o-mini', 'max_tokens' => 256, 'response_format' => [ 'type' => 'json_schema', 'json_schema' => [ 'name' => 'sentiment_result', 'strict' => true, 'schema' => [ 'type' => 'object', 'properties' => [ 'label' => ['type' => 'string', 'enum' => ['positive','neutral','negative']], 'score' => ['type' => 'number'], 'reason' => ['type' => 'string'], ], 'required' => ['label','score','reason'], 'additionalProperties'=> false, ], ], ], 'messages' => [ ['role' => 'system', 'content' => 'Analyse sentiment. Reply only with the JSON schema.'], ['role' => 'user', 'content' => $text], ], ])->throw()->json(); $raw = json_decode( $response['choices'][0]['message']['content'], true, flags: JSON_THROW_ON_ERROR ); return SentimentResult::fromArray($raw); } } ``` With `strict: true` the model is constrained to the schema at the API level — you still validate on your side, but you'll rarely see a mismatch. --- Keeping the Domain Clean ------------------------ The `SentimentAgent` returns a typed DTO. Nothing in your domain layer touches raw LLM strings. If you swap providers tomorrow, only the agent changes — every caller keeps working. Wrap the agent in a queued job for non-interactive workloads, and inject it via the service container so Pest can swap in a fake: ```php // tests/Feature/SentimentTest.php it('classifies positive text', function () { $this->instance(SentimentAgent::class, new class { public function analyse(string $text): SentimentResult { return new SentimentResult('positive', 0.95, 'stub'); } }); $result = app(SentimentAgent::class)->analyse('Great product!'); expect($result->label)->toBe('positive'); }); ``` --- Takeaways --------- - Stream via `response()->stream()` and set `X-Accel-Buffering: no` for Nginx. - Enforce token budgets *before* the HTTP call, not after. - Use `response_format.json_schema` with `strict: true` to pin model output shape. - Map LLM JSON to typed readonly DTOs immediately — keep raw strings out of your domain. - Inject agents through the container so tests can swap fakes without HTTP calls. 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&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) Frequently Asked Questions ---------------------------- 3 questions Q01 Does `response\_format: json\_schema` work with all OpenAI models? No. As of the current API, structured output with `strict: true` is supported on `gpt-4o`, `gpt-4o-mini`, and later snapshots. Older models like `gpt-3.5-turbo` support `json_object` mode only, which does not enforce a schema. Q02 How do I handle streaming in a queued job rather than an HTTP response? In a job you don't need SSE. Disable streaming (`stream: false`), collect the full completion, then persist or broadcast the result. Streaming is only valuable when a human is waiting in real time. Q03 Is the 4-characters-per-token heuristic accurate enough for production? It's a conservative approximation for English text. For precise budgeting, use a tokeniser library such as `yethee/tiktoken` which implements the actual BPE encoding. The heuristic is fine as a cheap pre-flight guard. Continue reading More Articles --------------- [ View all ](https://msaied.com/articles) [ ![Multi-Tenant SaaS in Laravel: Isolating Tenant State with Scoped Singletons](https://cdn.msaied.com/263/0ead3161989557874b88d47f8a9e023a.png) laravel multi-tenant saas ### Multi-Tenant SaaS in Laravel: Isolating Tenant State with Scoped Singletons Shared-state bugs are the silent killers of multi-tenant Laravel apps. 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