Streaming AI Responses in Laravel with Server-Sent Events

A developer upgraded a Laravel AI chatbot to stream responses using Server-Sent Events (SSE), leveraging Laravel 11's built-in `response()->eventStream()` method. The approach eliminates the 5-second wait for full completion by showing tokens as they arrive, improving perceived performance without extra infrastructure like WebSockets. The implementation uses the OpenAI PHP client's `createStreamed()` method and requires releasing the session lock to prevent blocking other requests.

In the first post of this series https://dev.to/adityakdevin/adding-an-ai-chatbot-to-your-laravel-app-with-the-openai-api-177f we built a working AI chatbot in Laravel. It had one problem every user notices immediately: you send a message, then stare at a spinner for five seconds while the whole response generates. LLMs produce text token by token. If you wait for the full completion before showing anything, you're throwing away the single biggest UX win available: streaming . The same answer that takes 5 seconds to finish starts appearing in ~300ms when streamed. Nothing about the model got faster — but to the user, it feels 10x faster. In this post we'll upgrade the chatbot to stream responses with Server-Sent Events SSE — no WebSockets, no Pusher, no extra infrastructure. Just Laravel. WebSockets are bidirectional and need a long-running server Reverb, Soketi or a paid service. For chat completions you only need one direction : server → browser, for the lifetime of one request. That's exactly what SSE is for, and since Laravel 11 it's built into the framework as response - eventStream . Rule of thumb: presence, typing indicators, multiplayer → WebSockets. Streaming one AI answer → SSE. We keep the ChatService from part 1 and add a streaming method. The openai-php client exposes createStreamed , which returns an iterator of deltas: php <?php namespace App\Services; use Generator; use OpenAI\Laravel\Facades\OpenAI; class ChatService { // ... SYSTEM PROMPT and reply from part 1 ... / @param array<int, array{role: string, content: string} $history / public function streamReply array $history, string $userMessage : Generator { $stream = OpenAI::chat - createStreamed 'model' = 'gpt-4o-mini', 'messages' = 'role' = 'system', 'content' = self::SYSTEM PROMPT , ...$history, 'role' = 'user', 'content' = $userMessage , , 'max tokens' = 500, ; foreach $stream as $response { $delta = $response- choices 0 - delta- content; if $delta == null { yield $delta; } } } } A Generator is the right return type here: the controller can forward chunks as they arrive without ever holding the full response in memory. Laravel 11.19+ ships response - eventStream , which handles the SSE formatting event: / data: lines for you: php <?php namespace App\Http\Controllers; use App\Services\ChatService; use Illuminate\Http\Request; class ChatStreamController extends Controller { public function invoke Request $request, ChatService $chat { $validated = $request- validate 'message' = 'required', 'string', 'max:2000' , ; $history = $request- session - get 'chat history', ; // CRITICAL: release the session lock. A streaming response is a // long-lived request — with the default file/database session // driver it would block every other request from this user // including page loads until the stream finishes. $request- session - save ; return response - eventStream function use $request, $chat, $history, $validated { $full = ''; foreach $chat- streamReply $history, $validated 'message' as $chunk { $full .= $chunk; yield $chunk; } // persist history once the stream completes $history = 'role' = 'user', 'content' = $validated 'message' ; $history = 'role' = 'assistant', 'content' = $full ; $request- session - put 'chat history', array slice $history, -20 ; $request- session - save ; }, headers: 'X-Accel-Buffering' = 'no', // tell nginx not to buffer the stream ; } } Route it with the same throttling as part 1: php Route::post '/chat/stream', ChatStreamController::class - middleware 'auth', 'throttle:20,1' ; The native EventSource API only supports GET requests, and we need to POST a message body — so we read the stream with fetch instead: js <script async function streamChat message, botEl { const res = await fetch '/chat/stream', { method: 'POST', headers: { 'Content-Type': 'application/json', 'X-CSRF-TOKEN': document.querySelector 'meta name="csrf-token" ' .content, }, body: JSON.stringify { message } , } ; const reader = res.body.getReader ; const decoder = new TextDecoder ; let buffer = ''; while true { const { done, value } = await reader.read ; if done break; buffer += decoder.decode value, { stream: true } ; // SSE messages are separated by a blank line const events = buffer.split '\n\n' ; buffer = events.pop ; // keep the incomplete tail for const evt of events { const data = evt.split '\n' .filter l = l.startsWith 'data: ' .map l = l.slice 6 .join '\n' ; if data && data == '</stream ' { botEl.textContent += data; } } } } </script </stream is Laravel's default end-of-stream marker — filter it out or customize it with the endStreamWith argument . That's it. Send a message and watch the answer type itself out. This is the part most tutorials skip. Streaming works instantly on php artisan serve , then mysteriously arrives all-at-once on your server. The culprit is always buffering somewhere between PHP and the browser : X-Accel-Buffering: no header above disables it per-response; alternatively set proxy buffering off; for the route. output buffering in php.ini . Laravel's eventStream flushes after every yield, but an outer buffer can still swallow it. curl -N against production before blaming your code.Debug tip: curl -N -X POST https://yourapp.test/chat/stream ... shows you exactly what arrives and when, with no browser magic in between. X-Accel-Buffering: no max execution time and your web server's send timeout. Set both above your worst-case generation time 60s is a sane ceiling with max tokens: 500 .Our bot streams beautifully, but it still only knows what's in its system prompt. Next up: RAG in Laravel — embeddings and pgvector , where we'll give it your actual documentation to answer from. Follow me to catch it. I'm Aditya Kumar adityakdevin — Tech Lead & full-stack developer building AI-powered web products with Laravel, Vue, and LLM APIs. Find me at adityadev.in.