Beyond Real-Time: The Power of Asynchronous Webhook Processing

# Synchronous Push: Must respond in <10 seconds or timeout
# Asynchronous Pull: Process at your own pace

import requests
import time

def async_processor():
    events = requests.get(
        "https://api.fetchhook.app/api/v1/stash_123",
        headers={"Authorization": "Bearer fh_xxx"}
    ).json().get('events', [])

    for event in events:
        # Take 30 seconds if needed - no timeout pressure
        result = slow_llm_analysis(event['payload'])
        save_to_database(result)
        time.sleep(2)  # Rate limit yourself

#What is the 'Push' Wall?

The 'Push' model assumes your consumer is always ready. But in reality, scripts crash, networks flicker, and LLMs take time to reason. When you're hit with 100 webhooks at once, a synchronous listener will either timeout or crash your local environment. This is the 'Push Wall.' Stripe gives you 10 seconds to respond, GitHub gives you 10 seconds, Shopify gives you 5 seconds. If your LLM takes 15 seconds to analyze a webhook payload, the event is marked as failed and retried—creating a cascading failure loop.

#How does FetchHook act as a shock absorber?

FetchHook accepts 1,000 webhooks per second and stashes them securely in your private mailbox. Your agent can then 'sip' from that stash—pulling 1 event at a time, processing it with an LLM, and then going back for the next. This asynchronous pattern turns a bursty, unstable event stream into a smooth, manageable workflow. The webhook sender gets an instant HTTP 202 response (happy), and your agent processes events without time pressure (also happy).

1. Burst Event: 100 webhooks arrive in 2 seconds
2. Ingress: FetchHook accepts all 100, returns HTTP 202
3. Buffer: Events stored in encrypted mailbox
4. Egress: Your script pulls @ 1 event/sec
5. Process: LLM analyzes each event (15 sec/event)
6. Success: Zero timeouts, zero data loss, zero retries

#Why is this critical for LLM-powered agents?

Large Language Models are slow. A GPT-4 call can take 10-30 seconds depending on context length. If you're processing webhook data through an LLM (e.g., analyzing GitHub issues, extracting payment intents, classifying support tickets), you cannot do this synchronously within a webhook handler. The connection will timeout. With FetchHook, you pull the event, spend as long as needed with the LLM, and there's no timeout pressure.

const OpenAI = require('openai');
const axios = require('axios');

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

async function processWithLLM() {
  // Pull events from mailbox
  const { data } = await axios.get(
    'https://api.fetchhook.app/api/v1/stash_ai',
    { headers: { Authorization: `Bearer ${process.env.FH_API_KEY}` } }
  );

  for (const event of data.events) {
    console.log(`Analyzing event: ${event.id}`);

    // LLM takes 15-20 seconds - no problem with async pull
    const analysis = await openai.chat.completions.create({
      model: 'gpt-4',
      messages: [{
        role: 'user',
        content: `Analyze this webhook: ${JSON.stringify(event.payload)}`
      }]
    });

    // Store result
    await saveAnalysis(event.id, analysis.choices[0].message.content);
  }
}

#How do I handle burst traffic without a queue system?

Traditional webhook architectures require Redis, RabbitMQ, or SQS to handle bursts. FetchHook eliminates this. The mailbox IS your queue. When Black Friday hits and 10,000 Shopify orders arrive in an hour, FetchHook buffers them all. Your script pulls and processes them at a sustainable rate (e.g., 1 per second), and the queue drains naturally. No queue infrastructure to manage, no worker pools to configure.

import requests
import time

def rate_limited_consumer():
    """
    Process events at 1 per second to respect downstream API limits.
    No external queue needed - FetchHook is the queue.
    """
    while True:
        events = requests.get(
            "https://api.fetchhook.app/api/v1/stash_orders",
            headers={"Authorization": "Bearer fh_live_xxx"}
        ).json().get('events', [])

        if not events:
            print("Queue empty, sleeping...")
            time.sleep(30)
            continue

        for event in events:
            # Process at controlled rate
            process_order(event['payload'])

            # Rate limit: 1 event/sec
            time.sleep(1)

        print(f"Processed {len(events)} events")

#What happens if my script crashes mid-processing?

With synchronous webhooks, a crash means lost events. With FetchHook, events stay in the mailbox until successfully consumed. If your script crashes after processing 50 out of 100 events, those 50 are marked as consumed (gone), and the remaining 50 are still in the queue. When your script restarts, it picks up exactly where it left off. No replay logic needed, no duplicate processing.