Sending PingspingA request sent to TelemHQ after a job runs. A ping can be a simple heartbeat or include JSON payload data about what happened.View glossary entrySource: TelemHQ docs to TelemHQ

The Basics

Each trackertrackerA monitored job, AI pipeline, worker, script, or automation in TelemHQ. Each tracker has its own ping URL and run history.View glossary entrySource: TelemHQ docs has a unique ping URL. Send a POST request after your job runs. Add a JSONJSONA common text format for structured data. TelemHQ accepts JSON payloads so jobs can report fields like status, tokens, duration, and cost.View glossary entrySource: MDN glossary body when you want TelemHQ to store metrics like tokenstokensThe pieces of text an AI model processes. Token counts are often used to measure usage and calculate model cost.View glossary entrySource: OpenAI token guide, costcostThe money associated with a run, often estimated from token usage and provider pricing. TelemHQ can store cost fields when your job sends them.View glossary entrySource: OpenAI token guide, duration, records processed, error counts, or custom fields.

PayloadpayloadThe structured data sent with a request. In TelemHQ, payloads should contain safe operational metadata, not prompts, completions, secrets, customer data, or private paths.View glossary entrySource: MDN API glossary Example

Payloads are optional, but they are the fastest way to turn a heartbeatheartbeatA lightweight signal that proves a job checked in. TelemHQ extends heartbeats with payload data so the run can explain what happened.View glossary entrySource: TelemHQ docs into a useful run log.

{
  "status": "success",
  "duration": 1500,
  "records_processed": 450,
  "errors": 0
}

A browser GETGETAn HTTP method usually used to retrieve data. In TelemHQ docs, GET requests are used for pages and read-style endpoints.View glossary entrySource: MDN HTTP docs request to a ping URL returns endpointendpointA specific URL where an API receives requests. A TelemHQ tracking URL is the endpoint your job calls after it runs.View glossary entrySource: MDN API glossary instructions. POST requests record runs.

curl -X POST https://telemhq.com/ping/YOUR_TOKEN

curl -X POST https://telemhq.com/ping/YOUR_TOKEN \
  -H "Content-Type: application/json" \
  -d '{"duration": 1500, "status": "success"}'

const axios = require('axios');
async function runJob() {
  // ... run your job logic ...
  
  await axios.post('https://telemhq.com/ping/YOUR_TOKEN');
}

runJob();

const axios = require('axios');

async function runJob() {
  try {
    // ... run your job logic ...

    await axios.post('https://telemhq.com/ping/YOUR_TOKEN', {
      duration: 120,
      items: 50,
      status: 'success'
    });
  } catch (error) {
    console.error(error);
  }
}

runJob();

import requests

# ... do work ...

requests.post('https://telemhq.com/ping/YOUR_TOKEN')

import requests
import time

start_time = time.time()

# ... do work ...

duration = time.time() - start_time

requests.post('https://telemhq.com/ping/YOUR_TOKEN', json={
  'duration': duration,
  'status': 'complete'
})

$url = 'https://telemhq.com/ping/YOUR_TOKEN';

// ... do work ...

file_get_contents($url, false, stream_context_create([
  'http' => ['method' => 'POST', 'timeout' => 10]
]));

$url = 'https://telemhq.com/ping/YOUR_TOKEN';
$data = ['records' => 500, 'memory' => memory_get_usage()];

$options = [
  'http' => [
    'header' => "Content-type: application/json\r\n",
    'method' => 'POST',
    'content' => json_encode($data),
    'timeout' => 10
  ]
];

$context = stream_context_create($options);
file_get_contents($url, false, $context);

package main

import "net/http"

func main() {
  // ... run job ...
  
  http.Post("https://telemhq.com/ping/YOUR_TOKEN", "application/json", nil)
}

package main

import (
  "bytes"
  "encoding/json"
  "net/http"
)

func main() {
  // ... run job ...
  
  values := map[string]interface{}{
    "status": "ok",
    "duration": 120
  }
  jsonData, _ := json.Marshal(values)
  
  http.Post("https://telemhq.com/ping/YOUR_TOKEN", "application/json",
    bytes.NewBuffer(jsonData))
}

AI Pipeline Guides

Use the provider guides when you want copy-paste examples for token usage, modelmodelThe AI system that processes input and returns output. For monitoring, the model name helps explain which tool or provider produced a run and how its token usage should be priced.View glossary entrySource: Anthropic model docs names, latencylatencyHow long a request or job takes to respond. AI job latency helps teams spot slow model calls, overloaded workers, or expensive retries.View glossary entrySource: MDN glossary, quality scores, and output counts.

Payload AssertionsassertionA rule that checks payload data after a run. Assertions can flag a job as unhealthy even if the process technically completed.View glossary entrySource: TelemHQ docs

Assertions mark a tracker as failing when the job pings but the payload does not match the expected shape. They are useful for AI jobs that finish successfully at the process level but process zero items, exceed a cost budget, or fail a quality threshold.

{
  "provider": "openai",
  "model": "gpt-4.1-mini",
  "status": "success",
  "input_tokens": 8120,
  "output_tokens": 940,
  "cost_usd": 0.42,
  "items_processed": 128,
  "items_failed": 0,
  "eval_score": 0.91
}

See our dedicated Webhooks Documentation for full configuration details and payload examples.