In the first post of this series 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.
Why SSE and not WebSockets?
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.
1. Stream from the OpenAI client
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
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.
2. The streaming controller
Laravel 11.19+ ships response()->eventStream(), which handles the SSE formatting (event: / data: lines) for you:
<?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:
Route::post('/chat/stream', ChatStreamController::class)
->middleware(['auth', 'throttle:20,1']);
3. Reading the stream in the browser
The native EventSource API only supports GET requests, and we need to POST a message body — so we read the stream with fetch instead:
<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.
Where streaming breaks in production
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:
- nginx buffers proxied responses by default. The
X-Accel-Buffering: noheader above disables it per-response; alternatively setproxy_buffering off;for the route. - PHP-FPM + output buffering: check
output_bufferinginphp.ini. Laravel'seventStreamflushes after every yield, but an outer buffer can still swallow it. - Cloudflare / load balancers: most respect SSE content types, but verify with
curl -Nagainst 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.
Production checklist
- Release the session lock before streaming (done above) — this is the #1 "my app hangs" bug with SSE in Laravel.
X-Accel-Buffering: noheader for nginx (done above).- Timeouts: a stream can outlive
max_execution_timeand your web server's send timeout. Set both above your worst-case generation time (60s is a sane ceiling withmax_tokens: 500). - Mid-stream errors: wrap the generator loop in try/catch and yield a friendly "Something went wrong" chunk — a dead stream with no message looks frozen to users.
- Rate limit the endpoint (done above) — streaming doesn't change the fact that every request costs money.
- Don't stream everything: for background jobs or short answers, the buffered endpoint from part 1 is simpler. Keep both.
Next in this series
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.